From 53b5111df7b46a52d54070983e689e4469b3a92f Mon Sep 17 00:00:00 2001 From: Fabian Becker Date: Tue, 28 Jan 2014 20:37:15 +0100 Subject: [PATCH] Refactored all m_ from eva2.optimization.strategies --- .../individuals/codings/gp/GPNodeOutput.java | 2 +- .../mocco/paretofrontviewer/MOCCOViewer.java | 4 +- .../paretofrontviewer/ParetoFrontView2D.java | 2 +- .../operator/archiving/AbstractArchiving.java | 4 +- .../migration/MOClusteringSeparation.java | 14 +- .../operator/migration/MOConeSeparation.java | 12 +- .../migration/MOXMeansSeparation.java | 4 +- .../operator/moso/MOSOMaxiMin.java | 8 +- ...ractMultiObjectiveOptimizationProblem.java | 2 +- src/eva2/optimization/strategies/ANPSO.java | 16 +- src/eva2/optimization/strategies/BOA.java | 22 +- .../strategies/BinaryScatterSearch.java | 22 +- .../strategies/ClusterBasedNichingEA.java | 8 +- .../strategies/ClusteringHillClimbing.java | 100 +++--- .../strategies/DifferentialEvolution.java | 44 +-- .../DynamicParticleSwarmOptimization.java | 50 ++- .../strategies/FloodAlgorithm.java | 144 ++++---- .../strategies/GradientDescentAlgorithm.java | 70 ++-- .../optimization/strategies/HillClimbing.java | 37 --- .../strategies/IslandModelEA.java | 307 +++++++++--------- src/eva2/optimization/strategies/LTGA.java | 8 +- src/eva2/optimization/strategies/MLTGA.java | 22 +- .../strategies/MemeticAlgorithm.java | 79 +++-- .../strategies/MultiObjectiveCMAES.java | 122 +++---- .../strategies/MultiObjectiveEA.java | 94 +++--- .../strategies/NelderMeadSimplex.java | 102 +++--- .../optimization/strategies/NichePSO.java | 148 ++++----- .../ParticleFilterOptimization.java | 97 +++--- .../ParticleSubSwarmOptimization.java | 32 +- .../strategies/ParticleSwarmOptimization.java | 139 ++++---- .../PopulationBasedIncrementalLearning.java | 174 +++++----- .../strategies/ScatterSearch.java | 48 +-- .../strategies/SimulatedAnnealing.java | 152 ++++----- .../strategies/ThresholdAlgorithm.java | 146 ++++----- src/eva2/optimization/strategies/Tribes.java | 70 ++-- .../strategies/WingedMultiObjectiveEA.java | 176 +++++----- 36 files changed, 1195 insertions(+), 1286 deletions(-) diff --git a/src/eva2/optimization/individuals/codings/gp/GPNodeOutput.java b/src/eva2/optimization/individuals/codings/gp/GPNodeOutput.java index 0642dbba..9ce7f0d7 100644 --- a/src/eva2/optimization/individuals/codings/gp/GPNodeOutput.java +++ b/src/eva2/optimization/individuals/codings/gp/GPNodeOutput.java @@ -97,6 +97,6 @@ public class GPNodeOutput extends AbstractGPNode implements java.io.Serializable // * @return string // */ // public String getStringRepresentation() { -// return ("( A:" +this.m_Identifier + " )"); +// return ("( A:" +this.identifier + " )"); // } } \ No newline at end of file diff --git a/src/eva2/optimization/mocco/paretofrontviewer/MOCCOViewer.java b/src/eva2/optimization/mocco/paretofrontviewer/MOCCOViewer.java index 12cf6c1b..1dce1ce5 100644 --- a/src/eva2/optimization/mocco/paretofrontviewer/MOCCOViewer.java +++ b/src/eva2/optimization/mocco/paretofrontviewer/MOCCOViewer.java @@ -642,7 +642,7 @@ public class MOCCOViewer extends JPanel implements InterfaceRefSolutionListener, // gbc.gridx = 3; // gbc.gridy = i+1; // this.m_JCShow[i] = new JCheckBox(); -// this.m_JCShow[i].setSelected(this.m_MOCCO.m_State.m_Show[i]); +// this.m_JCShow[i].setSelected(this.m_MOCCO.m_State.show[i]); // this.m_JCShow[i].addActionListener(showModeChanged); // this.m_History.add(this.m_JCShow[i], gbc); // gbc.gridx = 4; @@ -663,7 +663,7 @@ public class MOCCOViewer extends JPanel implements InterfaceRefSolutionListener, // ActionListener showModeChanged = new ActionListener() { // public void actionPerformed(ActionEvent event) { // for (int i = 0; i < m_JCShow.length; i++) { -// m_MOCCO.m_State.m_Show[i] = m_JCShow[i].isSelected(); +// m_MOCCO.m_State.show[i] = m_JCShow[i].isSelected(); // } // view.updateView(); // } diff --git a/src/eva2/optimization/mocco/paretofrontviewer/ParetoFrontView2D.java b/src/eva2/optimization/mocco/paretofrontviewer/ParetoFrontView2D.java index 7fccc92c..416b9407 100644 --- a/src/eva2/optimization/mocco/paretofrontviewer/ParetoFrontView2D.java +++ b/src/eva2/optimization/mocco/paretofrontviewer/ParetoFrontView2D.java @@ -197,7 +197,7 @@ public class ParetoFrontView2D extends JPanel implements InterfaceParetoFrontVie // first plot the populations if (false) { for (int i = 0; i < this.m_MOCCOViewer.m_MOCCO.m_State.m_PopulationHistory.length; i++) { - //System.out.println("Population " + i+" show " +this.m_MOCCOViewer.m_MOCCO.m_State.m_Show[i]); + //System.out.println("Population " + i+" show " +this.m_MOCCOViewer.m_MOCCO.m_State.show[i]); if (this.m_MOCCOViewer.m_MOCCO.m_State.m_Show[i]) { mySet = new GraphPointSet(i + 10, this.m_Area); mySet.setConnectedMode(false); diff --git a/src/eva2/optimization/operator/archiving/AbstractArchiving.java b/src/eva2/optimization/operator/archiving/AbstractArchiving.java index 3938e2f5..0f05a447 100644 --- a/src/eva2/optimization/operator/archiving/AbstractArchiving.java +++ b/src/eva2/optimization/operator/archiving/AbstractArchiving.java @@ -175,10 +175,10 @@ public abstract class AbstractArchiving implements InterfaceArchiving, java.io.S // * @param b True in case of the debug mode. // */ // public void setDebugFront(boolean b) { -// this.m_Debug = b; +// this.debug = b; // } // public boolean getDebugFront() { -// return this.m_Debug; +// return this.debug; // } // public String debugFrontTipText() { // return "Toggles the debug mode."; diff --git a/src/eva2/optimization/operator/migration/MOClusteringSeparation.java b/src/eva2/optimization/operator/migration/MOClusteringSeparation.java index a996af20..e42f7a47 100644 --- a/src/eva2/optimization/operator/migration/MOClusteringSeparation.java +++ b/src/eva2/optimization/operator/migration/MOClusteringSeparation.java @@ -32,7 +32,7 @@ import java.io.IOException; */ public class MOClusteringSeparation implements InterfaceMigration, java.io.Serializable { - public boolean m_Debug = false; + public boolean debug = false; private ClusteringKMeans m_KMeans = new ClusteringKMeans(); private ArchivingNSGAII m_NSGAII = new ArchivingNSGAII(); private boolean m_UseConstraints = true; @@ -44,7 +44,7 @@ public class MOClusteringSeparation implements InterfaceMigration, java.io.Seria } public MOClusteringSeparation(MOClusteringSeparation b) { - this.m_Debug = b.m_Debug; + this.debug = b.debug; this.m_UseConstraints = b.m_UseConstraints; if (b.m_KMeans != null) { this.m_KMeans = (ClusteringKMeans) b.m_KMeans.clone(); @@ -98,7 +98,7 @@ public class MOClusteringSeparation implements InterfaceMigration, java.io.Seria // collect the populations for (int i = 0; i < islands.length; i++) { oldIPOP[i] = islands[i].getPopulation(); - if (this.m_Debug) { + if (this.debug) { System.out.println("Got population from " + i + " of size " + oldIPOP[i].size()); } collector.addPopulation((Population) oldIPOP[i].clone()); @@ -106,7 +106,7 @@ public class MOClusteringSeparation implements InterfaceMigration, java.io.Seria } memory = (Population) collector.clone(); -// if (this.m_Debug) { +// if (this.debug) { // // let's see how they arrive here // Plot plot; // double[] tmpD = new double[2]; @@ -146,7 +146,7 @@ public class MOClusteringSeparation implements InterfaceMigration, java.io.Seria double[][] c = this.m_KMeans.getC(); newIPOP = this.m_KMeans.cluster(collector, c); - if (this.m_Debug) { + if (this.debug) { Plot plot; double[] tmpD = new double[2]; tmpD[0] = 0; @@ -199,7 +199,7 @@ public class MOClusteringSeparation implements InterfaceMigration, java.io.Seria } ConstBelongsToDifferentClass b = new ConstBelongsToDifferentClass(myClass, myOtherClass, this.m_KMeans.getUseSearchSpace()); ((AbstractMultiObjectiveOptimizationProblem) prob).m_AreaConst4Parallelization.add(b); -// if (this.m_Debug) { +// if (this.debug) { // String out = ""; // out += i+ ". MyClass: {"; // for (int j = 0; j < myClass.length; j++) out += myClass[j]+"; "; @@ -228,7 +228,7 @@ public class MOClusteringSeparation implements InterfaceMigration, java.io.Seria if (!oldIPOP[i].targetSizeReached()) { oldIPOP[i].addPopulation(this.m_Selection.selectFrom(memory, oldIPOP[i].getTargetSize() - oldIPOP[i].size())); } - if (this.m_Debug) { + if (this.debug) { System.out.println("Setting " + i + " to population size " + oldIPOP[i].size()); } islands[i].setPopulation(oldIPOP[i]); diff --git a/src/eva2/optimization/operator/migration/MOConeSeparation.java b/src/eva2/optimization/operator/migration/MOConeSeparation.java index 12ed6eb6..401fcd9b 100644 --- a/src/eva2/optimization/operator/migration/MOConeSeparation.java +++ b/src/eva2/optimization/operator/migration/MOConeSeparation.java @@ -34,7 +34,7 @@ import java.io.IOException; */ public class MOConeSeparation implements InterfaceMigration, java.io.Serializable { - public boolean m_Debug = false; + public boolean debug = false; private boolean m_UseAllToDetermineR = false; // since i'm only interessted in the pareto-front this should be set to false!! private boolean m_UseConstraints = true; private InterfaceSelection m_Selection = new SelectRandom(); @@ -45,7 +45,7 @@ public class MOConeSeparation implements InterfaceMigration, java.io.Serializabl } public MOConeSeparation(MOConeSeparation b) { - this.m_Debug = b.m_Debug; + this.debug = b.debug; this.m_UseConstraints = b.m_UseConstraints; this.m_UseAllToDetermineR = b.m_UseAllToDetermineR; if (b.m_Selection != null) { @@ -91,7 +91,7 @@ public class MOConeSeparation implements InterfaceMigration, java.io.Serializabl // collect the populations for (int i = 0; i < islands.length; i++) { oldIPOP[i] = islands[i].getPopulation(); - if (this.m_Debug) { + if (this.debug) { System.out.println("Got population from " + i + " of size " + oldIPOP[i].size()); } collector.addPopulation((Population) oldIPOP[i].clone()); @@ -118,7 +118,7 @@ public class MOConeSeparation implements InterfaceMigration, java.io.Serializabl if (!oldIPOP[i].targetSizeReached()) { oldIPOP[i].addPopulation(this.m_Selection.selectFrom(memory, oldIPOP[i].getTargetSize() - oldIPOP[i].size())); } - if (this.m_Debug) { + if (this.debug) { System.out.println("Setting island " + i + " to population size " + oldIPOP[i].size()); } allDom.addElementsToArchive(oldIPOP[i]); @@ -136,7 +136,7 @@ public class MOConeSeparation implements InterfaceMigration, java.io.Serializabl private void coneSeparation2D(Population collector, Population[] newIPOP, InterfaceOptimizer[] islands) { AbstractEAIndividual indy; -// if (this.m_Debug) { +// if (this.debug) { // // let's see how they arrive here // // This shows that the Drecksbeutels // // indeed spread out, even within @@ -212,7 +212,7 @@ public class MOConeSeparation implements InterfaceMigration, java.io.Serializabl // the rest belongs to newIPOP.length-1 newIPOP[newIPOP.length - 1].addPopulation(collector); - if (this.m_Debug) { + if (this.debug) { Plot plot; double[] tmpD = new double[2]; tmpD[0] = 0; diff --git a/src/eva2/optimization/operator/migration/MOXMeansSeparation.java b/src/eva2/optimization/operator/migration/MOXMeansSeparation.java index 746bc474..d61a009a 100644 --- a/src/eva2/optimization/operator/migration/MOXMeansSeparation.java +++ b/src/eva2/optimization/operator/migration/MOXMeansSeparation.java @@ -103,7 +103,7 @@ public class MOXMeansSeparation implements InterfaceMigration, java.io.Serializa } memory = (Population) collector.clone(); -// if (this.m_Debug) { +// if (this.debug) { // // let's see how they arrive here // Plot plot; // double[] tmpD = new double[2]; @@ -201,7 +201,7 @@ public class MOXMeansSeparation implements InterfaceMigration, java.io.Serializa } ConstBelongsToDifferentClass b = new ConstBelongsToDifferentClass(myClass, myOtherClass, this.m_XMeans.getUseSearchSpace()); ((AbstractMultiObjectiveOptimizationProblem) prob).m_AreaConst4Parallelization.add(b); -// if (this.m_Debug) { +// if (this.debug) { // String out = ""; // out += i+ ". MyClass: {"; // for (int j = 0; j < myClass.length; j++) out += myClass[j]+"; "; diff --git a/src/eva2/optimization/operator/moso/MOSOMaxiMin.java b/src/eva2/optimization/operator/moso/MOSOMaxiMin.java index 20546b33..4bf5f1e1 100644 --- a/src/eva2/optimization/operator/moso/MOSOMaxiMin.java +++ b/src/eva2/optimization/operator/moso/MOSOMaxiMin.java @@ -76,11 +76,11 @@ public class MOSOMaxiMin implements InterfaceMOSOConverter, java.io.Serializable } //////////////////////////////////////////////////////////////////////////////////// // if (false) { -// this.m_Plot = new eva2.gui.plot.Plot("Debug MaxiMin", "Y1", "Y2"); -// this.m_Plot.setUnconnectedPoint(0, 0, 11); -// this.m_Plot.setUnconnectedPoint(1.2, 2.0, 11); +// this.plot = new eva2.gui.plot.Plot("Debug MaxiMin", "Y1", "Y2"); +// this.plot.setUnconnectedPoint(0, 0, 11); +// this.plot.setUnconnectedPoint(1.2, 2.0, 11); // double[][] trueFitness, moFitness; -// GraphPointSet mySet = new GraphPointSet(10, this.m_Plot.getFunctionArea()); +// GraphPointSet mySet = new GraphPointSet(10, this.plot.getFunctionArea()); // DPoint myPoint; // double tmp1, tmp2; // Chart2DDPointIconText tmp; diff --git a/src/eva2/optimization/problems/AbstractMultiObjectiveOptimizationProblem.java b/src/eva2/optimization/problems/AbstractMultiObjectiveOptimizationProblem.java index 13ed424e..6686c9f8 100644 --- a/src/eva2/optimization/problems/AbstractMultiObjectiveOptimizationProblem.java +++ b/src/eva2/optimization/problems/AbstractMultiObjectiveOptimizationProblem.java @@ -381,7 +381,7 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract Population tmpPop = null; if (p.getGeneration() > 2) { -// m_Plot = new eva2.gui.plot.Plot("Multiobjective Optimization", "Y1", "Y2"); +// plot = new eva2.gui.plot.Plot("Multiobjective Optimization", "Y1", "Y2"); // i want to plot the pareto front for MOEA and other strategies // but i have to differentiate between the case where // there is a true MOEA at work and where the diff --git a/src/eva2/optimization/strategies/ANPSO.java b/src/eva2/optimization/strategies/ANPSO.java index 5d07a4c2..67463272 100644 --- a/src/eva2/optimization/strategies/ANPSO.java +++ b/src/eva2/optimization/strategies/ANPSO.java @@ -554,7 +554,7 @@ public class ANPSO extends NichePSO implements InterfaceOptimizer, InterfaceAddi if (reinitSuperfl) { for (int i = 0; i < tmpPop.size(); i++) { AbstractEAIndividual indy = tmpPop.getEAIndividual(i); - indy.init(m_Problem); + indy.init(optimizationProblem); indy.resetFitness(Double.MAX_VALUE); // TODO this is not so nice... they should be collected in a reinit-list and inserted at the beginning of the next optimize step ParticleSwarmOptimization.initIndividualDefaults(indy, 0.2); ParticleSwarmOptimization.initIndividualMemory(indy); @@ -802,10 +802,10 @@ public class ANPSO extends NichePSO implements InterfaceOptimizer, InterfaceAddi // } for (int i = 0; i < getSubSwarms().size(); ++i) { AbstractEAIndividual bestSS = ((ParticleSubSwarmOptimization) getSubSwarms().get(i)).getBestIndividual(); - elitePop.addIndividual((AbstractEAIndividual) ((ParticleSubSwarmOptimization) getSubSwarms().get(i)).m_BestIndividual.clone()); + elitePop.addIndividual((AbstractEAIndividual) ((ParticleSubSwarmOptimization) getSubSwarms().get(i)).bestIndividual.clone()); } for (int i = 0; i < inactiveSubSwarms.size(); ++i) { - elitePop.addIndividual((AbstractEAIndividual) ((ParticleSubSwarmOptimization) inactiveSubSwarms.get(i)).m_BestIndividual.clone()); + elitePop.addIndividual((AbstractEAIndividual) ((ParticleSubSwarmOptimization) inactiveSubSwarms.get(i)).bestIndividual.clone()); } return elitePop; } @@ -830,7 +830,7 @@ public class ANPSO extends NichePSO implements InterfaceOptimizer, InterfaceAddi */ @Override protected void plotSubSwarms() { - if (this.m_Problem instanceof Interface2DBorderProblem) { + if (this.optimizationProblem instanceof Interface2DBorderProblem) { //DPointSet popRep = new DPointSet(); InterfaceDataTypeDouble tmpIndy1; @@ -839,7 +839,7 @@ public class ANPSO extends NichePSO implements InterfaceOptimizer, InterfaceAddi // for all inactive SubSwarms from ANPSO... for (int i = 0; i < this.inactiveSubSwarms.size(); i++) { ParticleSubSwarmOptimization currentsubswarm = this.inactiveSubSwarms.get(i); - InterfaceDataTypeDouble best = (InterfaceDataTypeDouble) currentsubswarm.m_BestIndividual; + InterfaceDataTypeDouble best = (InterfaceDataTypeDouble) currentsubswarm.bestIndividual; plotCircleForIndy((AbstractEAIndividual) best, "[I]"); } @@ -848,7 +848,7 @@ public class ANPSO extends NichePSO implements InterfaceOptimizer, InterfaceAddi ParticleSubSwarmOptimization currentsubswarm = this.getSubSwarms().get(i); Population currentsubswarmpop = (Population) currentsubswarm.getPopulation(); //InterfaceDataTypeDouble best = (InterfaceDataTypeDouble)currentsubswarmpop.getBestIndividual(); - InterfaceDataTypeDouble best = (InterfaceDataTypeDouble) currentsubswarm.m_BestIndividual; + InterfaceDataTypeDouble best = (InterfaceDataTypeDouble) currentsubswarm.bestIndividual; DPointSet popRep = new DPointSet(); //...draw SubSwarm as points @@ -857,7 +857,7 @@ public class ANPSO extends NichePSO implements InterfaceOptimizer, InterfaceAddi tmpIndy1 = (InterfaceDataTypeDouble) currentsubswarmpop.get(j); popRep.addDPoint(new DPoint(tmpIndy1.getDoubleData()[0], tmpIndy1.getDoubleData()[1])); } - this.m_TopologySwarm.getFunctionArea().addDElement(popRep); // time consuming + this.topoPlot.getFunctionArea().addDElement(popRep); // time consuming //...draw circle for best if (!currentsubswarm.isActive()) { @@ -886,7 +886,7 @@ public class ANPSO extends NichePSO implements InterfaceOptimizer, InterfaceAddi popRep.addDPoint(new DPoint(tmpIndy1.getDoubleData()[0], tmpIndy1.getDoubleData()[1])); popRep.addDPoint(new DPoint(best.getDoubleData()[0], best.getDoubleData()[1])); } - this.m_TopologySwarm.getFunctionArea().addDElement(popRep); // time consuming + this.topoPlot.getFunctionArea().addDElement(popRep); // time consuming } } // endif } diff --git a/src/eva2/optimization/strategies/BOA.java b/src/eva2/optimization/strategies/BOA.java index 8c657805..baa3c0b3 100644 --- a/src/eva2/optimization/strategies/BOA.java +++ b/src/eva2/optimization/strategies/BOA.java @@ -37,8 +37,8 @@ import java.util.logging.Logger; @Description(value = "Basic implementation of the Bayesian Optimization Algorithm based on the works by Martin Pelikan and David E. Goldberg.") public class BOA implements InterfaceOptimizer, java.io.Serializable { private static final Logger LOGGER = Logger.getLogger(BOA.class.getName()); - transient private InterfacePopulationChangedEventListener m_Listener = null; - private String m_Identifier = "BOA"; + transient private InterfacePopulationChangedEventListener populationChangedEventListener = null; + private String identifier = "BOA"; private int probDim = 8; private int fitCrit = -1; private int PopSize = 50; @@ -82,8 +82,8 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable { } public BOA(BOA b) { - this.m_Listener = b.m_Listener; - this.m_Identifier = b.m_Identifier; + this.populationChangedEventListener = b.populationChangedEventListener; + this.identifier = b.identifier; this.probDim = b.probDim; this.fitCrit = b.fitCrit; this.PopSize = b.PopSize; @@ -126,7 +126,7 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable { @Override public void addPopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - this.m_Listener = ea; + this.populationChangedEventListener = ea; } private void createDirectoryIfNeeded(String directoryName) { @@ -141,8 +141,8 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable { @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener == ea) { - m_Listener = null; + if (populationChangedEventListener == ea) { + populationChangedEventListener = null; return true; } else { return false; @@ -546,8 +546,8 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable { * Something has changed */ protected void firePropertyChangedEvent(String name) { - if (this.m_Listener != null) { - this.m_Listener.registerPopulationStateChanged(this, name); + if (this.populationChangedEventListener != null) { + this.populationChangedEventListener.registerPopulationStateChanged(this, name); } } @@ -568,12 +568,12 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable { @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.identifier = name; } @Override public String getIdentifier() { - return this.m_Identifier; + return this.identifier; } @Override diff --git a/src/eva2/optimization/strategies/BinaryScatterSearch.java b/src/eva2/optimization/strategies/BinaryScatterSearch.java index 07f4c06f..cdcf96f8 100644 --- a/src/eva2/optimization/strategies/BinaryScatterSearch.java +++ b/src/eva2/optimization/strategies/BinaryScatterSearch.java @@ -32,8 +32,8 @@ import java.util.BitSet; public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializable, InterfacePopulationChangedEventListener { private static boolean TRACE = false; - transient private InterfacePopulationChangedEventListener m_Listener = null; - private String m_Identifier = "BinaryScatterSearch"; + transient private InterfacePopulationChangedEventListener populationChangedEventListener = null; + private String identifier = "BinaryScatterSearch"; private int MaxImpIter = 5; private int poolSize = 100; private int refSetSize = 10; @@ -64,8 +64,8 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ * @param b */ public BinaryScatterSearch(BinaryScatterSearch b) { - this.m_Listener = b.m_Listener; - this.m_Identifier = b.m_Identifier; + this.populationChangedEventListener = b.populationChangedEventListener; + this.identifier = b.identifier; this.MaxImpIter = b.MaxImpIter; this.poolSize = b.poolSize; this.refSetSize = b.refSetSize; @@ -153,14 +153,14 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ @Override public void addPopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - this.m_Listener = ea; + this.populationChangedEventListener = ea; } @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener == ea) { - m_Listener = null; + if (populationChangedEventListener == ea) { + populationChangedEventListener = null; return true; } else { return false; @@ -773,12 +773,12 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.identifier = name; } @Override public String getIdentifier() { - return this.m_Identifier; + return this.identifier; } @Override @@ -797,8 +797,8 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ } protected void firePropertyChangedEvent(String name) { - if (this.m_Listener != null) { - this.m_Listener.registerPopulationStateChanged(this, name); + if (this.populationChangedEventListener != null) { + this.populationChangedEventListener.registerPopulationStateChanged(this, name); } } diff --git a/src/eva2/optimization/strategies/ClusterBasedNichingEA.java b/src/eva2/optimization/strategies/ClusterBasedNichingEA.java index 32ecf965..4661fa86 100644 --- a/src/eva2/optimization/strategies/ClusterBasedNichingEA.java +++ b/src/eva2/optimization/strategies/ClusterBasedNichingEA.java @@ -574,7 +574,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis // if (isActive(curSpecies)) { // Lets have only active species... if ((haltingWindow > 0) && (this.testSpeciesForConvergence(curSpecies))) { ///////////////////////////////////////////// Halting Window ///////////////////////////////////////////////// -// if (this.m_Debug) { +// if (this.debug) { // System.out.println("Undiff.Size: " + this.undifferentiatedPopulation.size() +"/"+this.undifferentiatedPopulation.getPopulationSize()); // System.out.println("Diff.Size : " + ((Population)this.species.get(i)).size() +"/"+((Population)this.species.get(i)).getPopulationSize()); // } @@ -617,11 +617,11 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis undifferentiatedPopulation.addPopulation(initializeIndividuals(toReinit)); undifferentiatedPopulation.incrFunctionCallsBy(toReinit); -// if (this.m_Debug) { +// if (this.debug) { // System.out.println("Undiff.Size: " + this.undifferentiatedPopulation.size() +"/"+this.undifferentiatedPopulation.getPopulationSize()); // System.out.println("Diff.Size : " + ((Population)this.species.get(i)).size() +"/"+((Population)this.species.get(i)).getPopulationSize()); // } -// if (this.m_Debug) System.out.println("--------------------------End converged"); +// if (this.debug) System.out.println("--------------------------End converged"); ////////////////////////////////////////////////////////////////////////////////////////////////////////////// } else { // actually optimize D_i @@ -718,7 +718,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis printState("---After differentiation", reinitPop); } - //if (this.m_Show) this.plot(); + //if (this.show) this.plot(); } // end of species differentiation // plot the populations diff --git a/src/eva2/optimization/strategies/ClusteringHillClimbing.java b/src/eva2/optimization/strategies/ClusteringHillClimbing.java index 7528f5d2..ba3095f7 100644 --- a/src/eva2/optimization/strategies/ClusteringHillClimbing.java +++ b/src/eva2/optimization/strategies/ClusteringHillClimbing.java @@ -39,12 +39,12 @@ import java.io.Serializable; public class ClusteringHillClimbing implements InterfacePopulationChangedEventListener, InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer { - transient private InterfacePopulationChangedEventListener m_Listener; + transient private InterfacePopulationChangedEventListener populationChangedEventListener; public static final boolean TRACE = false; - transient private String m_Identifier = ""; - private Population m_Population = new Population(); + transient private String identifier = ""; + private Population population = new Population(); private transient Population archive = new Population(); - private InterfaceOptimizationProblem m_Problem = new F1Problem(); + private InterfaceOptimizationProblem optimizationProblem = new F1Problem(); private int hcEvalCycle = 1000; private int initialPopSize = 100; private int loopCnt = 0; @@ -72,8 +72,8 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi public ClusteringHillClimbing(ClusteringHillClimbing other) { hideHideable(); - m_Population = (Population) other.m_Population.clone(); - m_Problem = (InterfaceOptimizationProblem) other.m_Problem.clone(); + population = (Population) other.population.clone(); + optimizationProblem = (InterfaceOptimizationProblem) other.optimizationProblem.clone(); hcEvalCycle = other.hcEvalCycle; initialPopSize = other.initialPopSize; @@ -103,12 +103,12 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.identifier = name; } @Override public String getIdentifier() { - return this.m_Identifier; + return this.identifier; } /** @@ -118,24 +118,24 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi */ @Override public void setProblem(InterfaceOptimizationProblem problem) { - this.m_Problem = problem; + this.optimizationProblem = problem; } @Override public InterfaceOptimizationProblem getProblem() { - return this.m_Problem; + return this.optimizationProblem; } @Override public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { - this.m_Listener = ea; + this.populationChangedEventListener = ea; } @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener == ea) { - m_Listener = null; + if (populationChangedEventListener == ea) { + populationChangedEventListener = null; return true; } else { return false; @@ -148,10 +148,10 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi mutator = new MutateESFixedStepSize(initialStepSize); archive = new Population(); hideHideable(); - m_Population.setTargetSize(initialPopSize); - this.m_Problem.initializePopulation(this.m_Population); - m_Population.addPopulationChangedEventListener(null); // noone will be notified directly on pop changes - this.m_Problem.evaluate(this.m_Population); + population.setTargetSize(initialPopSize); + this.optimizationProblem.initializePopulation(this.population); + population.addPopulationChangedEventListener(null); // noone will be notified directly on pop changes + this.optimizationProblem.evaluate(this.population); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } @@ -164,11 +164,11 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi @Override public void initByPopulation(Population pop, boolean reset) { loopCnt = 0; - this.m_Population = (Population) pop.clone(); - m_Population.addPopulationChangedEventListener(null); + this.population = (Population) pop.clone(); + population.addPopulationChangedEventListener(null); if (reset) { - this.m_Population.init(); - this.m_Problem.evaluate(this.m_Population); + this.population.init(); + this.optimizationProblem.evaluate(this.population); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } } @@ -177,8 +177,8 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi * Something has changed */ protected void firePropertyChangedEvent(String name) { - if (this.m_Listener != null) { - this.m_Listener.registerPopulationStateChanged(this, name); + if (this.populationChangedEventListener != null) { + this.populationChangedEventListener.registerPopulationStateChanged(this, name); } } @@ -187,13 +187,13 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi double improvement; loopCnt++; - m_Population.addPopulationChangedEventListener(this); - m_Population.setNotifyEvalInterval(notifyGuiEvery); + population.addPopulationChangedEventListener(this); + population.setNotifyEvalInterval(notifyGuiEvery); Pair popD; - int funCallsBefore = m_Population.getFunctionCalls(); - int evalsNow, lastOverhead = (m_Population.getFunctionCalls() % hcEvalCycle); + int funCallsBefore = population.getFunctionCalls(); + int evalsNow, lastOverhead = (population.getFunctionCalls() % hcEvalCycle); if (lastOverhead > 0) { - evalsNow = (2 * hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle)); + evalsNow = (2 * hcEvalCycle - (population.getFunctionCalls() % hcEvalCycle)); } else { evalsNow = hcEvalCycle; } @@ -201,7 +201,7 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi if (TRACE) { System.out.println("evalCycle: " + hcEvalCycle + ", evals now: " + evalsNow); } - popD = PostProcess.clusterLocalSearch(localSearchMethod, m_Population, (AbstractOptimizationProblem) m_Problem, sigmaClust, evalsNow, 0.5, mutator); + popD = PostProcess.clusterLocalSearch(localSearchMethod, population, (AbstractOptimizationProblem) optimizationProblem, sigmaClust, evalsNow, 0.5, mutator); // (population, (AbstractOptimizationProblem)problem, sigmaClust, hcEvalCycle - (population.getFunctionCalls() % hcEvalCycle), 0.5); if (popD.head().getFunctionCalls() == funCallsBefore) { System.err.println("Bad case, increasing allowed evaluations!"); @@ -209,12 +209,12 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi } } while (popD.head().getFunctionCalls() == funCallsBefore); improvement = popD.tail(); - m_Population = popD.head(); + population = popD.head(); if (TRACE) { - System.out.println("num inds after clusterLS: " + m_Population.size()); + System.out.println("num inds after clusterLS: " + population.size()); } - popD.head().setGeneration(m_Population.getGeneration() + 1); + popD.head().setGeneration(population.getGeneration() + 1); if (doReinitialization && (improvement < minImprovement)) { if (TRACE) { @@ -231,21 +231,21 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi } // store results - archive.setFunctionCalls(m_Population.getFunctionCalls()); - archive.addPopulation(m_Population); + archive.setFunctionCalls(population.getFunctionCalls()); + archive.addPopulation(population); Population tmpPop = new Population(); tmpPop.addPopulationChangedEventListener(null); tmpPop.setTargetSize(initialPopSize); - this.m_Problem.initializePopulation(tmpPop); - tmpPop.setSameParams(m_Population); + this.optimizationProblem.initializePopulation(tmpPop); + tmpPop.setSameParams(population); tmpPop.setTargetSize(initialPopSize); - this.m_Problem.evaluate(tmpPop); + this.optimizationProblem.evaluate(tmpPop); // reset population while keeping function calls etc. - m_Population.clear(); - m_Population.addPopulation(tmpPop); - m_Population.incrFunctionCallsBy(tmpPop.size()); + population.clear(); + population.addPopulation(tmpPop); + population.incrFunctionCallsBy(tmpPop.size()); } else { // decrease step size for hc if (localSearchMethod != PostProcessMethod.hillClimber) { @@ -270,7 +270,7 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi // System.out.println("bla"); // } // set funcalls to real value - m_Population.setFunctionCalls(((Population) source).getFunctionCalls()); + population.setFunctionCalls(((Population) source).getFunctionCalls()); // System.out.println("FunCallIntervalReached at " + (((Population)source).getFunctionCalls())); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } @@ -288,12 +288,12 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi */ @Override public Population getPopulation() { - return this.m_Population; + return this.population; } @Override public void setPopulation(Population pop) { - this.m_Population = pop; + this.population = pop; } public String populationTipText() { @@ -304,11 +304,11 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi public InterfaceSolutionSet getAllSolutions() { Population tmp = new Population(); tmp.addPopulation(archive); - tmp.addPopulation(m_Population); - tmp.setFunctionCalls(m_Population.getFunctionCalls()); - tmp.setGeneration(m_Population.getGeneration()); + tmp.addPopulation(population); + tmp.setFunctionCalls(population.getFunctionCalls()); + tmp.setGeneration(population.getGeneration()); // tmp = PostProcessInterim.clusterBest(tmp, sigma, 0, PostProcessInterim.KEEP_LONERS, PostProcessInterim.BEST_ONLY); - return new SolutionSet(m_Population, tmp); + return new SolutionSet(population, tmp); } /** @@ -323,8 +323,8 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi sbuf.append(", initial pop size: "); sbuf.append(getPopulation().getTargetSize()); sbuf.append("Optimization Problem: "); - sbuf.append(this.m_Problem.getStringRepresentationForProblem(this)); - sbuf.append(this.m_Population.getStringRepresentation()); + sbuf.append(this.optimizationProblem.getStringRepresentationForProblem(this)); + sbuf.append(this.population.getStringRepresentation()); return sbuf.toString(); } @@ -485,7 +485,7 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi @Override public Object[] getAdditionalDataValue(PopulationInterface pop) { - return new Object[]{m_Population.size(), mutator.getSigma(), archive.size(), archive.getPopulationMeasures()[0]}; + return new Object[]{population.size(), mutator.getSigma(), archive.size(), archive.getPopulationMeasures()[0]}; } public boolean isDoReinitialization() { diff --git a/src/eva2/optimization/strategies/DifferentialEvolution.java b/src/eva2/optimization/strategies/DifferentialEvolution.java index 0fb7585c..5fc3683a 100644 --- a/src/eva2/optimization/strategies/DifferentialEvolution.java +++ b/src/eva2/optimization/strategies/DifferentialEvolution.java @@ -52,7 +52,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial @Parameter(name = "Lambda", description = "Enhance greediness through amplification of the differential vector to the best individual for DE2.") private double lambda = 0.6; - private double m_Mt = 0.05; + private double mt = 0.05; private int maximumAge = -1; private boolean reEvaluate = false; // to log the parents of a newly created indy. @@ -60,8 +60,8 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial private transient Vector parents = null; private boolean randomizeFKLambda = false; private boolean generational = true; - private String m_Identifier = ""; - transient private Vector m_Listener = new Vector(); + private String identifier = ""; + transient private Vector populationChangedEventListeners = new Vector<>(); private boolean forceRange = true; private boolean cyclePop = false; // if true, individuals are used as parents in a cyclic sequence - otherwise randomly private boolean compareToParent = true; // if true, the challenge indy is compared to its parent, otherwise to a random individual @@ -80,7 +80,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial differentialWeight = f; crossoverRate = k; this.lambda = lambda; - m_Mt = mt; + this.mt = mt; } /** @@ -92,11 +92,11 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial this.DEType = a.DEType; this.population = (Population) a.population.clone(); this.optimizationProblem = (AbstractOptimizationProblem) a.optimizationProblem.clone(); - this.m_Identifier = a.m_Identifier; + this.identifier = a.identifier; this.differentialWeight = a.differentialWeight; this.crossoverRate = a.crossoverRate; this.lambda = a.lambda; - this.m_Mt = a.m_Mt; + this.mt = a.mt; this.maximumAge = a.maximumAge; this.randomizeFKLambda = a.randomizeFKLambda; @@ -393,7 +393,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial if (parents != null) { parents.add(pop.getEAIndividual(parentIndex)); } // Add wherever oX is used directly - if (RNG.flipCoin(this.m_Mt)) { + if (RNG.flipCoin(this.mt)) { double[] xk, xl; double p, pj, pk, pl; InterfaceDataTypeDouble indy1 = null, indy2 = null; @@ -679,16 +679,16 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial */ @Override public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { - if (this.m_Listener == null) { - this.m_Listener = new Vector(); + if (this.populationChangedEventListeners == null) { + this.populationChangedEventListeners = new Vector(); } - this.m_Listener.add(ea); + this.populationChangedEventListeners.add(ea); } @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener != null && m_Listener.removeElement(ea)) { + if (populationChangedEventListeners != null && populationChangedEventListeners.removeElement(ea)) { return true; } else { @@ -702,9 +702,9 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial * @param name */ protected void firePropertyChangedEvent(String name) { - if (this.m_Listener != null) { - for (int i = 0; i < this.m_Listener.size(); i++) { - this.m_Listener.get(i).registerPopulationStateChanged(this, name); + if (this.populationChangedEventListeners != null) { + for (int i = 0; i < this.populationChangedEventListeners.size(); i++) { + this.populationChangedEventListeners.get(i).registerPopulationStateChanged(this, name); } } } @@ -747,12 +747,12 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial */ @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.identifier = name; } @Override public String getIdentifier() { - return this.m_Identifier; + return this.identifier; } /** @@ -854,17 +854,17 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial * @param l */ public void setMt(double l) { - this.m_Mt = l; - if (this.m_Mt < 0) { - this.m_Mt = 0; + this.mt = l; + if (this.mt < 0) { + this.mt = 0; } - if (this.m_Mt > 1) { - this.m_Mt = 1; + if (this.mt > 1) { + this.mt = 1; } } public double getMt() { - return this.m_Mt; + return this.mt; } public String mtTipText() { diff --git a/src/eva2/optimization/strategies/DynamicParticleSwarmOptimization.java b/src/eva2/optimization/strategies/DynamicParticleSwarmOptimization.java index 08309339..8a6a8040 100644 --- a/src/eva2/optimization/strategies/DynamicParticleSwarmOptimization.java +++ b/src/eva2/optimization/strategies/DynamicParticleSwarmOptimization.java @@ -164,44 +164,44 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization resetFitness(indy); plotIndy(position, null, (Integer) (indy.getData(indexKey))); -// if (this.m_Show) { -// this.m_Plot.setUnconnectedPoint(position[0], position[1], (Integer)(indy.getData(indexKey))); - //this.m_Plot.setConnectedPoint(curPosition[0] + curVelocity[0], curPosition[1] + curVelocity[1], index+1); +// if (this.show) { +// this.plot.setUnconnectedPoint(position[0], position[1], (Integer)(indy.getData(indexKey))); + //this.plot.setConnectedPoint(curPosition[0] + curVelocity[0], curPosition[1] + curVelocity[1], index+1); // } //System.out.println("quantum particle " + index + " set to " + newPos[0] + "/" + newPos[1]); } private void plotBestIndy() { - if (m_Plot != null) { + if (plot != null) { double[] curPosition = ((InterfaceDataTypeDouble) population.getBestEAIndividual()).getDoubleData(); if (lastBestPlot != null) { - this.m_Plot.setConnectedPoint(lastBestPlot[0], lastBestPlot[1], 0); + this.plot.setConnectedPoint(lastBestPlot[0], lastBestPlot[1], 0); } - this.m_Plot.setConnectedPoint(curPosition[0], curPosition[1], 0); + this.plot.setConnectedPoint(curPosition[0], curPosition[1], 0); lastBestPlot = curPosition.clone(); } } @Override protected void plotIndy(double[] curPosition, double[] curVelocity, int index) { - if (this.m_Show) { + if (this.show) { if (plotBestOnly) { if (index != ((Integer) (population.getBestEAIndividual().getData(indexKey)))) { return; } else { -// if (lastBestPlot != null) this.m_Plot.setConnectedPoint(lastBestPlot[0], lastBestPlot[1], index); -// this.m_Plot.setConnectedPoint(curPosition[0], curPosition[1], index); - this.m_Plot.setUnconnectedPoint(curPosition[0], curPosition[1], index); +// if (lastBestPlot != null) this.plot.setConnectedPoint(lastBestPlot[0], lastBestPlot[1], index); +// this.plot.setConnectedPoint(curPosition[0], curPosition[1], index); + this.plot.setUnconnectedPoint(curPosition[0], curPosition[1], index); lastBestPlot = curPosition.clone(); } } else { if (curVelocity == null) { - this.m_Plot.setUnconnectedPoint(curPosition[0], curPosition[1], index); + this.plot.setUnconnectedPoint(curPosition[0], curPosition[1], index); } else { - this.m_Plot.setConnectedPoint(curPosition[0], curPosition[1], index); - this.m_Plot.setConnectedPoint(curPosition[0] + curVelocity[0], curPosition[1] + curVelocity[1], index); + this.plot.setConnectedPoint(curPosition[0], curPosition[1], index); + this.plot.setConnectedPoint(curPosition[0] + curVelocity[0], curPosition[1] + curVelocity[1], index); } } } @@ -253,14 +253,6 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization if (envHasChanged) { double chi; double[] curVelocity = new double[lastVelocity.length]; - /* old ways - curVelocity[i] = this.m_Inertness * velocity[i]; - - curVelocity[i] += (this.phi1 * getSpeedLimit(index) * (range[i][1] - range[i][0]) * RNG.randomDouble(-1., 1.)); - // the component from the social model - curVelocity[i] += this.phi2*RNG.randomDouble(0,1)*(localBestPos[i]-curPosition[i]); - - */ for (int i = 0; i < lastVelocity.length; i++) { // the component from the old velocity curVelocity[i] = this.inertnessOrChi * lastVelocity[i]; @@ -401,7 +393,7 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization protected void evaluatePopulation(Population population) { envHasChanged = false; super.evaluatePopulation(population); - if (m_Show && plotBestOnly) { + if (show && plotBestOnly) { plotBestIndy(); } @@ -427,11 +419,11 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization @Override protected void logBestIndividual() { // log the best individual of the population - if (envHasChanged || (this.population.getBestEAIndividual().isDominatingDebConstraints(this.m_BestIndividual))) { - this.m_BestIndividual = (AbstractEAIndividual) this.population.getBestEAIndividual().clone(); - this.m_BestIndividual.putData(partBestFitKey, this.m_BestIndividual.getFitness()); - this.m_BestIndividual.putData(partBestPosKey, ((InterfaceDataTypeDouble) this.m_BestIndividual).getDoubleData()); - //System.out.println("-- best ind set to " + ((InterfaceDataTypeDouble)this.m_BestIndividual).getDoubleData()[0] + "/" + ((InterfaceDataTypeDouble)this.m_BestIndividual).getDoubleData()[1]); + if (envHasChanged || (this.population.getBestEAIndividual().isDominatingDebConstraints(this.bestIndividual))) { + this.bestIndividual = (AbstractEAIndividual) this.population.getBestEAIndividual().clone(); + this.bestIndividual.putData(partBestFitKey, this.bestIndividual.getFitness()); + this.bestIndividual.putData(partBestPosKey, ((InterfaceDataTypeDouble) this.bestIndividual).getDoubleData()); + //System.out.println("-- best ind set to " + ((InterfaceDataTypeDouble)this.bestIndividual).getDoubleData()[0] + "/" + ((InterfaceDataTypeDouble)this.bestIndividual).getDoubleData()[1]); } } @@ -499,7 +491,7 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization public String getStringRepresentation() { StringBuilder strB = new StringBuilder(200); strB.append("Dynamic Particle Swarm Optimization:\nOptimization Problem: "); - strB.append(this.m_Problem.getStringRepresentationForProblem(this)); + strB.append(this.optimizationProblem.getStringRepresentationForProblem(this)); strB.append("\n"); strB.append(this.population.getStringRepresentation()); return strB.toString(); @@ -686,7 +678,7 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization } public Plot getPlot() { - return m_Plot; + return plot; } /** diff --git a/src/eva2/optimization/strategies/FloodAlgorithm.java b/src/eva2/optimization/strategies/FloodAlgorithm.java index fb22526b..9c998c1a 100644 --- a/src/eva2/optimization/strategies/FloodAlgorithm.java +++ b/src/eva2/optimization/strategies/FloodAlgorithm.java @@ -23,28 +23,28 @@ import eva2.util.annotation.Description; public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable { // These variables are necessary for the simple testcase - private InterfaceOptimizationProblem m_Problem = new B1Problem(); - private int m_MultiRuns = 100; - private int m_FitnessCalls = 100; - private int m_FitnessCallsNeeded = 0; - GAIndividualBinaryData m_Best, m_Test; - public double m_InitialFloodPeak = 2000.0, m_CurrentFloodPeak; - public double m_DrainRate = 1.0; + private InterfaceOptimizationProblem optimizationProblem = new B1Problem(); + private int multiRuns = 100; + private int fitnessCalls = 100; + private int fitnessCallsNeeded = 0; + GAIndividualBinaryData bestIndividual, testIndividual; + public double initialFloodPeak = 2000.0, currentFloodPeak; + public double drainRate = 1.0; // These variables are necessary for the more complex LectureGUI enviroment - transient private String m_Identifier = ""; - transient private InterfacePopulationChangedEventListener m_Listener; - private Population m_Population; + transient private String identifier = ""; + transient private InterfacePopulationChangedEventListener listener; + private Population population; public FloodAlgorithm() { - this.m_Population = new Population(); - this.m_Population.setTargetSize(10); + this.population = new Population(); + this.population.setTargetSize(10); } public FloodAlgorithm(FloodAlgorithm a) { - this.m_Population = (Population) a.m_Population.clone(); - this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone(); - this.m_InitialFloodPeak = a.m_InitialFloodPeak; - this.m_DrainRate = a.m_DrainRate; + this.population = (Population) a.population.clone(); + this.optimizationProblem = (InterfaceOptimizationProblem) a.optimizationProblem.clone(); + this.initialFloodPeak = a.initialFloodPeak; + this.drainRate = a.drainRate; } @Override @@ -57,9 +57,9 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable */ @Override public void init() { - this.m_Problem.initializePopulation(this.m_Population); - this.m_Problem.evaluate(this.m_Population); - this.m_CurrentFloodPeak = this.m_InitialFloodPeak; + this.optimizationProblem.initializePopulation(this.population); + this.optimizationProblem.evaluate(this.population); + this.currentFloodPeak = this.initialFloodPeak; this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } @@ -70,13 +70,13 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable */ @Override public void initByPopulation(Population pop, boolean reset) { - this.m_Population = (Population) pop.clone(); + this.population = (Population) pop.clone(); if (reset) { - this.m_Population.init(); - this.m_Problem.evaluate(this.m_Population); + this.population.init(); + this.optimizationProblem.evaluate(this.population); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } - this.m_CurrentFloodPeak = this.m_InitialFloodPeak; + this.currentFloodPeak = this.initialFloodPeak; } /** @@ -85,26 +85,26 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable @Override public void optimize() { AbstractEAIndividual indy; - Population original = (Population) this.m_Population.clone(); + Population original = (Population) this.population.clone(); double[] fitness; - for (int i = 0; i < this.m_Population.size(); i++) { - indy = ((AbstractEAIndividual) this.m_Population.get(i)); + for (int i = 0; i < this.population.size(); i++) { + indy = ((AbstractEAIndividual) this.population.get(i)); double tmpD = indy.getMutationProbability(); indy.setMutationProbability(1.0); indy.mutate(); indy.setMutationProbability(tmpD); } - this.m_Problem.evaluate(this.m_Population); - for (int i = 0; i < this.m_Population.size(); i++) { - fitness = ((AbstractEAIndividual) this.m_Population.get(i)).getFitness(); - if (fitness[0] > this.m_CurrentFloodPeak) { - this.m_Population.remove(i); - this.m_Population.add(i, original.get(i)); + this.optimizationProblem.evaluate(this.population); + for (int i = 0; i < this.population.size(); i++) { + fitness = ((AbstractEAIndividual) this.population.get(i)).getFitness(); + if (fitness[0] > this.currentFloodPeak) { + this.population.remove(i); + this.population.add(i, original.get(i)); } } - this.m_CurrentFloodPeak -= this.m_DrainRate; - this.m_Population.incrGeneration(); + this.currentFloodPeak -= this.drainRate; + this.population.incrGeneration(); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } @@ -132,36 +132,36 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable */ @Override public void setProblem(InterfaceOptimizationProblem problem) { - this.m_Problem = problem; + this.optimizationProblem = problem; } @Override public InterfaceOptimizationProblem getProblem() { - return this.m_Problem; + return this.optimizationProblem; } /** * This method will init the HillClimber */ public void defaultInit() { - this.m_FitnessCallsNeeded = 0; - this.m_Best = new GAIndividualBinaryData(); - this.m_Best.defaultInit(m_Problem); + this.fitnessCallsNeeded = 0; + this.bestIndividual = new GAIndividualBinaryData(); + this.bestIndividual.defaultInit(optimizationProblem); } /** * This method will optimize */ public void defaultOptimize() { - for (int i = 0; i < m_FitnessCalls; i++) { - this.m_Test = (GAIndividualBinaryData) ((this.m_Best).clone()); - this.m_Test.defaultMutate(); - if (this.m_Test.defaultEvaulateAsMiniBits() < this.m_Best.defaultEvaulateAsMiniBits()) { - this.m_Best = this.m_Test; + for (int i = 0; i < fitnessCalls; i++) { + this.testIndividual = (GAIndividualBinaryData) ((this.bestIndividual).clone()); + this.testIndividual.defaultMutate(); + if (this.testIndividual.defaultEvaulateAsMiniBits() < this.bestIndividual.defaultEvaulateAsMiniBits()) { + this.bestIndividual = this.testIndividual; } - this.m_FitnessCallsNeeded = i; - if (this.m_Best.defaultEvaulateAsMiniBits() == 0) { - i = this.m_FitnessCalls + 1; + this.fitnessCallsNeeded = i; + if (this.bestIndividual.defaultEvaulateAsMiniBits() == 0) { + i = this.fitnessCalls + 1; } } } @@ -174,15 +174,15 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable public static void main(String[] args) { FloodAlgorithm program = new FloodAlgorithm(); int TmpMeanCalls = 0, TmpMeanFitness = 0; - for (int i = 0; i < program.m_MultiRuns; i++) { + for (int i = 0; i < program.multiRuns; i++) { program.defaultInit(); program.defaultOptimize(); - TmpMeanCalls += program.m_FitnessCallsNeeded; - TmpMeanFitness += program.m_Best.defaultEvaulateAsMiniBits(); + TmpMeanCalls += program.fitnessCallsNeeded; + TmpMeanFitness += program.bestIndividual.defaultEvaulateAsMiniBits(); } - TmpMeanCalls /= program.m_MultiRuns; - TmpMeanFitness /= program.m_MultiRuns; - System.out.println("(" + program.m_MultiRuns + "/" + program.m_FitnessCalls + ") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls); + TmpMeanCalls /= program.multiRuns; + TmpMeanFitness /= program.multiRuns; + System.out.println("(" + program.multiRuns + "/" + program.fitnessCalls + ") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls); } /** @@ -192,14 +192,14 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable */ @Override public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { - this.m_Listener = ea; + this.listener = ea; } @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener == ea) { - m_Listener = null; + if (listener == ea) { + listener = null; return true; } else { return false; @@ -210,8 +210,8 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable * Something has changed */ protected void firePropertyChangedEvent(String name) { - if (this.m_Listener != null) { - this.m_Listener.registerPopulationStateChanged(this, name); + if (this.listener != null) { + this.listener.registerPopulationStateChanged(this, name); } } @@ -224,14 +224,14 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable @Override public String getStringRepresentation() { String result = ""; - if (this.m_Population.size() > 1) { - result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n"; + if (this.population.size() > 1) { + result += "Multi(" + this.population.size() + ")-Start Hill Climbing:\n"; } else { result += "Simulated Annealing:\n"; } result += "Optimization Problem: "; - result += this.m_Problem.getStringRepresentationForProblem(this) + "\n"; - result += this.m_Population.getStringRepresentation(); + result += this.optimizationProblem.getStringRepresentationForProblem(this) + "\n"; + result += this.population.getStringRepresentation(); return result; } @@ -242,12 +242,12 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable */ @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.identifier = name; } @Override public String getIdentifier() { - return this.m_Identifier; + return this.identifier; } /** @@ -269,12 +269,12 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable */ @Override public Population getPopulation() { - return this.m_Population; + return this.population; } @Override public void setPopulation(Population pop) { - this.m_Population = pop; + this.population = pop; } public String populationTipText() { @@ -293,11 +293,11 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable * @return The initial flood level. */ public double getInitialFloodPeak() { - return this.m_InitialFloodPeak; + return this.initialFloodPeak; } public void setInitialFloodPeak(double pop) { - this.m_InitialFloodPeak = pop; + this.initialFloodPeak = pop; } public String initialFloodPeakTipText() { @@ -311,13 +311,13 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable * @return The drain rate. */ public double getDrainRate() { - return this.m_DrainRate; + return this.drainRate; } public void setDrainRate(double a) { - this.m_DrainRate = a; - if (this.m_DrainRate < 0) { - this.m_DrainRate = 0.0; + this.drainRate = a; + if (this.drainRate < 0) { + this.drainRate = 0.0; } } diff --git a/src/eva2/optimization/strategies/GradientDescentAlgorithm.java b/src/eva2/optimization/strategies/GradientDescentAlgorithm.java index 73076d48..a3ce6dd6 100644 --- a/src/eva2/optimization/strategies/GradientDescentAlgorithm.java +++ b/src/eva2/optimization/strategies/GradientDescentAlgorithm.java @@ -24,8 +24,8 @@ import eva2.util.annotation.Description; @Description("Gradient Descent can be applied to derivable functions (InterfaceFirstOrderDerivableProblem).") public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Serializable { - private InterfaceOptimizationProblem m_Problem; - InterfaceDataTypeDouble m_Best, m_Test; + private InterfaceOptimizationProblem optimizationProblem; + InterfaceDataTypeDouble bestDataTypeDouble, testDataTypeDouble; private int iterations = 1; private double wDecreaseStepSize = 0.5; private double wIncreaseStepSize = 1.1; @@ -41,12 +41,12 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser double localmaxstepsize = 10; double localminstepsize = 1e-10; private boolean momentumterm = false; - transient private InterfacePopulationChangedEventListener m_Listener; + transient private InterfacePopulationChangedEventListener populationChangedEventListener; public double maximumabsolutechange = 0.2; // Hashtable indyhash; // These variables are necessary for the more complex LectureGUI enviroment - transient private String m_Identifier = ""; - private Population m_Population; + transient private String identifier = ""; + private Population population; private static boolean TRACE = false; private static final String lockKey = "gdaLockDataKey"; private static final String lastFitnessKey = "gdaLastFitDataKey"; @@ -61,7 +61,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser this.setPopulation((Population) pop.clone()); if (reset) { this.getPopulation().init(); - this.m_Problem.evaluate(this.getPopulation()); + this.optimizationProblem.evaluate(this.getPopulation()); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } //System.out.println("initByPopulation() called"); @@ -70,8 +70,8 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser public GradientDescentAlgorithm() { // indyhash = new Hashtable(); - this.m_Population = new Population(); - this.m_Population.setTargetSize(1); + this.population = new Population(); + this.population.setTargetSize(1); } /** @@ -108,8 +108,8 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser public void init() { //System.out.println("init() called "); // indyhash = new Hashtable(); - this.m_Problem.initializePopulation(this.m_Population); - this.m_Problem.evaluate(this.m_Population); + this.optimizationProblem.initializePopulation(this.population); + this.optimizationProblem.evaluate(this.population); } public double signum(double val) { @@ -122,8 +122,8 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser AbstractEAIndividual indy; // if ((this.indyhash == null) || (this.indyhash.size() <1)) init(); - for (int i = 0; i < this.m_Population.size(); i++) { - indy = ((AbstractEAIndividual) this.m_Population.get(i)); + for (int i = 0; i < this.population.size(); i++) { + indy = ((AbstractEAIndividual) this.population.get(i)); if (!indy.hasData(gradientKey)) { //System.out.println("new indy to hash"); // Hashtable history = new Hashtable(); @@ -146,9 +146,9 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser } } // System.out.println("hashtable built"); - for (int i = 0; i < this.m_Population.size(); i++) { + for (int i = 0; i < this.population.size(); i++) { - indy = ((AbstractEAIndividual) this.m_Population.get(i)); + indy = ((AbstractEAIndividual) this.population.get(i)); double[][] range = ((InterfaceDataTypeDouble) indy).getDoubleRange(); double[] params = ((InterfaceDataTypeDouble) indy).getDoubleData(); indy.putData(oldParamsKey, params); @@ -157,7 +157,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser double indystepsize = ((Double) indy.getData(stepSizeKey)).doubleValue(); // System.out.println("indystepsize" + indystepsize); - if ((this.m_Problem instanceof InterfaceFirstOrderDerivableProblem) && (indy instanceof InterfaceDataTypeDouble)) { + if ((this.optimizationProblem instanceof InterfaceFirstOrderDerivableProblem) && (indy instanceof InterfaceDataTypeDouble)) { // Hashtable history = (Hashtable) indyhash.get(indy); for (int iterations = 0; iterations < this.iterations; iterations++) { @@ -165,7 +165,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser double[] wstepsize = (double[]) indy.getData(wStepSizeKey); double[] oldchange = null; - double[] gradient = ((InterfaceFirstOrderDerivableProblem) m_Problem).getFirstOrderGradients(params); + double[] gradient = ((InterfaceFirstOrderDerivableProblem) optimizationProblem).getFirstOrderGradients(params); if (TRACE) { System.out.println("GDA: " + BeanInspector.toString(params) + ", grad: " + BeanInspector.toString(gradient)); } @@ -245,12 +245,12 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser } } // for loop population size - this.m_Problem.evaluate(this.m_Population); - m_Population.incrGeneration(); + this.optimizationProblem.evaluate(this.population); + population.incrGeneration(); if (this.recovery) { - for (int i = 0; i < this.m_Population.size(); i++) { - indy = ((AbstractEAIndividual) this.m_Population.get(i)); + for (int i = 0; i < this.population.size(); i++) { + indy = ((AbstractEAIndividual) this.population.get(i)); // Hashtable history = (Hashtable) indyhash.get(indy); if (indy.getFitness()[0] > recoverythreshold) { if (TRACE) { @@ -273,15 +273,15 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser } else { } } - this.m_Problem.evaluate(this.m_Population); - m_Population.incrGeneration(); + this.optimizationProblem.evaluate(this.population); + population.incrGeneration(); } if (this.globalStepSizeAdaption) { //System.out.println("gsa main"); - for (int i = 0; i < this.m_Population.size(); i++) { - indy = ((AbstractEAIndividual) this.m_Population.get(i)); + for (int i = 0; i < this.population.size(); i++) { + indy = ((AbstractEAIndividual) this.population.get(i)); // Hashtable history = (Hashtable) indyhash.get(indy); // if (history == null) break; if (indy.getData(lastFitnessKey) != null) { @@ -312,14 +312,14 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser private double momentumweigth = 0.1; protected void firePropertyChangedEvent(String name) { - if (this.m_Listener != null) { - this.m_Listener.registerPopulationStateChanged(this, name); + if (this.populationChangedEventListener != null) { + this.populationChangedEventListener.registerPopulationStateChanged(this, name); } } @Override public Population getPopulation() { - return this.m_Population; + return this.population; } @Override @@ -334,7 +334,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser // if (indyhash.contains(pop.get(i))) newindyhash.put(pop.get(i), indyhash.get(pop.get(i))); // } // indyhash = newindyhash; - this.m_Population = pop; + this.population = pop; } /** @@ -344,23 +344,23 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser */ @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.identifier = name; } @Override public String getIdentifier() { - return this.m_Identifier; + return this.identifier; } @Override public void setProblem(InterfaceOptimizationProblem problem) { - m_Problem = problem; + optimizationProblem = problem; } @Override public InterfaceOptimizationProblem getProblem() { - return m_Problem; + return optimizationProblem; } @Override @@ -370,14 +370,14 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser @Override public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { - this.m_Listener = ea; + this.populationChangedEventListener = ea; } @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener == ea) { - m_Listener = null; + if (populationChangedEventListener == ea) { + populationChangedEventListener = null; return true; } else { return false; diff --git a/src/eva2/optimization/strategies/HillClimbing.java b/src/eva2/optimization/strategies/HillClimbing.java index c79950da..f503eb9d 100644 --- a/src/eva2/optimization/strategies/HillClimbing.java +++ b/src/eva2/optimization/strategies/HillClimbing.java @@ -140,43 +140,6 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable { return this.optimizationProblem; } -// /** This method will init the HillClimber -// */ -// public void defaultInit() { -// this.m_FitnessCallsNeeded = 0; -// this.m_Best = new GAIndividualBinaryData(); -// this.m_Best.defaultInit(); -// } -// -// /** This method will optimize -// */ -// public void defaultOptimize() { -// for (int i = 0; i < maxFitnessCalls; i++) { -// this.m_Test = (GAIndividualBinaryData)((this.m_Best).clone()); -// this.m_Test.defaultMutate(); -// if (this.m_Test.defaultEvaulateAsMiniBits() < this.m_Best.defaultEvaulateAsMiniBits()) this.m_Best = this.m_Test; -// this.m_FitnessCallsNeeded = i; -// if (this.m_Best.defaultEvaulateAsMiniBits() == 0) i = this.maxFitnessCalls +1; -// } -// } -// /** This main method will start a simple hillclimber. -// * No arguments necessary. -// * @param args -// */ -// public static void main(String[] args) { -// HillClimbing program = new HillClimbing(); -// int TmpMeanCalls = 0, TmpMeanFitness = 0; -// for (int i = 0; i < program.m_MultiRuns; i++) { -// program.defaultInit(); -// program.defaultOptimize(); -// TmpMeanCalls += program.m_FitnessCallsNeeded; -// TmpMeanFitness += program.m_Best.defaultEvaulateAsMiniBits(); -// } -// TmpMeanCalls = TmpMeanCalls/program.m_MultiRuns; -// TmpMeanFitness = TmpMeanFitness/program.m_MultiRuns; -// System.out.println("("+program.m_MultiRuns+"/"+program.maxFitnessCalls+") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls); -// } - /** * This method allows you to add the LectureGUI as listener to the Optimizer * diff --git a/src/eva2/optimization/strategies/IslandModelEA.java b/src/eva2/optimization/strategies/IslandModelEA.java index dd74751a..4a4c7167 100644 --- a/src/eva2/optimization/strategies/IslandModelEA.java +++ b/src/eva2/optimization/strategies/IslandModelEA.java @@ -34,37 +34,37 @@ import eva2.util.annotation.Description; @Description("This is an island model EA distributing the individuals across several (remote) CPUs for optimization.") public class IslandModelEA implements InterfacePopulationChangedEventListener, InterfaceOptimizer, java.io.Serializable { - private Population m_Population = new Population(); - private InterfaceOptimizer m_Optimizer = new GeneticAlgorithm(); - private InterfaceMigration m_Migration = new SOBestMigration(); - private InterfaceOptimizationProblem m_Problem = new F8Problem(); + private Population population = new Population(); + private InterfaceOptimizer optimizer = new GeneticAlgorithm(); + private InterfaceMigration migration = new SOBestMigration(); + private InterfaceOptimizationProblem optimizationProblem = new F8Problem(); // private String[] m_NodesList; - private int m_MigrationRate = 10; + private int migrationRate = 10; private boolean heterogeneousProblems = false; // These are the processor to run on - private int m_numLocalCPUs = 1; - private boolean m_localOnly = false; - transient private InterfaceOptimizer[] m_Islands; + private int numLocalCPUs = 1; + private boolean numLocalOnly = false; + transient private InterfaceOptimizer[] islands; // This is for debugging - private boolean m_LogLocalChanges = true; - private boolean m_Show = false; - transient private Plot m_Plot = null; - transient private String m_Identifier = ""; - transient private InterfacePopulationChangedEventListener m_Listener; + private boolean logLocalChanges = true; + private boolean show = false; + transient private Plot plot = null; + transient private String identifier = ""; + transient private InterfacePopulationChangedEventListener populationChangedEventListener; transient private final boolean TRACE = false; public IslandModelEA() { } public IslandModelEA(IslandModelEA a) { - this.m_Population = (Population) a.m_Population.clone(); - this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone(); - this.m_Optimizer = (InterfaceOptimizer) a.m_Optimizer.clone(); - this.m_Migration = (InterfaceMigration) a.m_Migration.clone(); - this.m_MigrationRate = a.m_MigrationRate; + this.population = (Population) a.population.clone(); + this.optimizationProblem = (InterfaceOptimizationProblem) a.optimizationProblem.clone(); + this.optimizer = (InterfaceOptimizer) a.optimizer.clone(); + this.migration = (InterfaceMigration) a.migration.clone(); + this.migrationRate = a.migrationRate; this.heterogeneousProblems = a.heterogeneousProblems; - this.m_numLocalCPUs = a.m_numLocalCPUs; - this.m_localOnly = a.m_localOnly; + this.numLocalCPUs = a.numLocalCPUs; + this.numLocalOnly = a.numLocalOnly; } @Override @@ -74,31 +74,31 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I @Override public void init() { - if (this.m_Show) { - if (this.m_Plot == null) { + if (this.show) { + if (this.plot == null) { double[] tmpD = new double[2]; tmpD[0] = 0; tmpD[1] = 0; - this.m_Plot = new Plot("Island Model EA", "FitnessCalls", "Fitness", tmpD, tmpD); + this.plot = new Plot("Island Model EA", "FitnessCalls", "Fitness", tmpD, tmpD); } } // this.population = new Population(); - this.m_Population.clear(); - this.m_Population.init(); - this.m_Optimizer.init(); - this.m_Optimizer.setProblem(this.m_Problem); - this.m_Optimizer.setPopulation((Population) m_Population.clone()); + this.population.clear(); + this.population.init(); + this.optimizer.init(); + this.optimizer.setProblem(this.optimizationProblem); + this.optimizer.setPopulation((Population) population.clone()); InterfacePopulationChangedEventListener myLocal = null; - if (this.m_localOnly) { + if (this.numLocalOnly) { // this is running on the local machine - this.m_Islands = new InterfaceOptimizer[this.m_numLocalCPUs]; - for (int i = 0; i < this.m_numLocalCPUs; i++) { - this.m_Islands[i] = (InterfaceOptimizer) this.m_Optimizer.clone(); - this.m_Islands[i].setIdentifier("" + i); - this.m_Islands[i].init(); - if (this.m_LogLocalChanges) { - this.m_Islands[i].addPopulationChangedEventListener(this); + this.islands = new InterfaceOptimizer[this.numLocalCPUs]; + for (int i = 0; i < this.numLocalCPUs; i++) { + this.islands[i] = (InterfaceOptimizer) this.optimizer.clone(); + this.islands[i].setIdentifier("" + i); + this.islands[i].init(); + if (this.logLocalChanges) { + this.islands[i].addPopulationChangedEventListener(this); } } } else { @@ -116,30 +116,30 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I if ((nodesList == null) || (nodesList.length == 0)) { throw new RuntimeException("Error, no active remote servers available! Activate servers or use localOnly mode."); } - this.m_Islands = new InterfaceOptimizer[nodesList.length]; + this.islands = new InterfaceOptimizer[nodesList.length]; for (int i = 0; i < nodesList.length; i++) { - this.m_Islands[i] = (InterfaceOptimizer) RMIProxyRemoteThread.newInstance(this.optimizer, nodesList[i]); - this.m_Islands[i].setIdentifier(""+i); - this.m_Islands[i].init(); - if (this.m_LogLocalChanges) { - this.m_Islands[i].addPopulationChangedEventListener(myLocal); + this.islands[i] = (InterfaceOptimizer) RMIProxyRemoteThread.newInstance(this.optimizer, nodesList[i]); + this.islands[i].setIdentifier(""+i); + this.islands[i].init(); + if (this.logLocalChanges) { + this.islands[i].addPopulationChangedEventListener(myLocal); } }*/ } - this.m_Migration.initMigration(this.m_Islands); + this.migration.initMigration(this.islands); Population pop; - this.m_Population.incrGeneration(); // the island-initialization has increased the island-pop generations. + this.population.incrGeneration(); // the island-initialization has increased the island-pop generations. - for (int i = 0; i < this.m_Islands.length; i++) { - pop = (Population) this.m_Islands[i].getPopulation().clone(); - this.m_Population.addPopulation(pop); - this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls()); - if (m_Islands[i].getPopulation().getGeneration() != m_Population.getGeneration()) { + for (int i = 0; i < this.islands.length; i++) { + pop = (Population) this.islands[i].getPopulation().clone(); + this.population.addPopulation(pop); + this.population.incrFunctionCallsBy(pop.getFunctionCalls()); + if (islands[i].getPopulation().getGeneration() != population.getGeneration()) { System.err.println("Error, inconsistent generations!"); } } - this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED, this.m_Optimizer.getPopulation()); + this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED, this.optimizer.getPopulation()); } /** @@ -150,32 +150,32 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I @Override public void initByPopulation(Population tpop, boolean reset) { // TODO this is again evil copy&paste style - if (this.m_Show) { - if (this.m_Plot == null) { + if (this.show) { + if (this.plot == null) { double[] tmpD = new double[2]; tmpD[0] = 0; tmpD[1] = 0; - this.m_Plot = new Plot("Island Model EA", "FitnessCalls", "Fitness", tmpD, tmpD); + this.plot = new Plot("Island Model EA", "FitnessCalls", "Fitness", tmpD, tmpD); } } - this.m_Population = (Population) tpop.clone(); + this.population = (Population) tpop.clone(); if (reset) { - this.m_Population.init(); - this.m_Population.incrGeneration(); + this.population.init(); + this.population.incrGeneration(); } - this.m_Optimizer.init(); - this.m_Optimizer.setProblem(this.m_Problem); + this.optimizer.init(); + this.optimizer.setProblem(this.optimizationProblem); InterfacePopulationChangedEventListener myLocal = null; - if (this.m_localOnly) { + if (this.numLocalOnly) { // this is running on the local machine - this.m_Islands = new InterfaceOptimizer[this.m_numLocalCPUs]; - for (int i = 0; i < this.m_numLocalCPUs; i++) { - this.m_Islands[i] = (InterfaceOptimizer) this.m_Optimizer.clone(); - this.m_Islands[i].setIdentifier("" + i); - this.m_Islands[i].init(); - if (this.m_LogLocalChanges) { - this.m_Islands[i].addPopulationChangedEventListener(this); + this.islands = new InterfaceOptimizer[this.numLocalCPUs]; + for (int i = 0; i < this.numLocalCPUs; i++) { + this.islands[i] = (InterfaceOptimizer) this.optimizer.clone(); + this.islands[i].setIdentifier("" + i); + this.islands[i].init(); + if (this.logLocalChanges) { + this.islands[i].addPopulationChangedEventListener(this); } } } else { @@ -194,25 +194,25 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I if ((nodesList == null) || (nodesList.length == 0)) { return; } - this.m_Islands = new InterfaceOptimizer[nodesList.length]; + this.islands = new InterfaceOptimizer[nodesList.length]; for (int i = 0; i < nodesList.length; i++) { - this.m_Islands[i] = (InterfaceOptimizer) RMIProxyRemoteThread.newInstance(this.optimizer, nodesList[i]); - this.m_Islands[i].setIdentifier(""+i); - this.m_Islands[i].init(); - if (this.m_LogLocalChanges) { - this.m_Islands[i].addPopulationChangedEventListener(myLocal); + this.islands[i] = (InterfaceOptimizer) RMIProxyRemoteThread.newInstance(this.optimizer, nodesList[i]); + this.islands[i].setIdentifier(""+i); + this.islands[i].init(); + if (this.logLocalChanges) { + this.islands[i].addPopulationChangedEventListener(myLocal); } }*/ } - this.m_Migration.initMigration(this.m_Islands); + this.migration.initMigration(this.islands); Population pop; - for (int i = 0; i < this.m_Islands.length; i++) { - pop = (Population) this.m_Islands[i].getPopulation().clone(); - this.m_Population.addPopulation(pop); - this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls()); + for (int i = 0; i < this.islands.length; i++) { + pop = (Population) this.islands[i].getPopulation().clone(); + this.population.addPopulation(pop); + this.population.incrFunctionCallsBy(pop.getFunctionCalls()); } - this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED, this.m_Optimizer.getPopulation()); + this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED, this.optimizer.getPopulation()); } /** @@ -220,27 +220,27 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I */ @Override public void optimize() { - for (int i = 0; i < this.m_Islands.length; i++) { - if (this.m_Islands[i].getPopulation().size() > 0) { - this.m_Islands[i].optimize(); + for (int i = 0; i < this.islands.length; i++) { + if (this.islands[i].getPopulation().size() > 0) { + this.islands[i].optimize(); if (TRACE) { - System.out.println(BeanInspector.toString(m_Islands[i].getPopulation())); + System.out.println(BeanInspector.toString(islands[i].getPopulation())); } } else { - this.m_Islands[i].getPopulation().incrGeneration(); + this.islands[i].getPopulation().incrGeneration(); } if (TRACE) { System.out.println("----"); } } - this.m_Population.incrGeneration(); - if ((this.m_Population.getGeneration() % this.m_MigrationRate) == 0) { + this.population.incrGeneration(); + if ((this.population.getGeneration() % this.migrationRate) == 0) { this.communicate(); } // this is necessary for heterogeneous islands if (this.heterogeneousProblems) { - for (int i = 0; i < this.m_Islands.length; i++) { - this.m_Islands[i].getProblem().evaluate(this.m_Islands[i].getPopulation()); + for (int i = 0; i < this.islands.length; i++) { + this.islands[i].getProblem().evaluate(this.islands[i].getPopulation()); } } System.gc(); @@ -252,13 +252,13 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I private void communicate() { // Here i'll have to wait until all islands are finished boolean allReachedG = false; - int G = this.m_Population.getGeneration(); + int G = this.population.getGeneration(); while (!allReachedG) { allReachedG = true; String gen = "["; - for (int i = 0; i < this.m_Islands.length; i++) { - gen += this.m_Islands[i].getPopulation().getGeneration() + "; "; - if (this.m_Islands[i].getPopulation().getGeneration() != G) { + for (int i = 0; i < this.islands.length; i++) { + gen += this.islands[i].getPopulation().getGeneration() + "; "; + if (this.islands[i].getPopulation().getGeneration() != G) { allReachedG = false; } } @@ -271,22 +271,22 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I } } } - this.m_Population.clear(); - this.m_Population.setFunctionCalls(0); + this.population.clear(); + this.population.setFunctionCalls(0); Population pop; - for (int i = 0; i < this.m_Islands.length; i++) { - pop = (Population) this.m_Islands[i].getPopulation().clone(); - this.m_Population.addPopulation(pop); - this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls()); + for (int i = 0; i < this.islands.length; i++) { + pop = (Population) this.islands[i].getPopulation().clone(); + this.population.addPopulation(pop); + this.population.incrFunctionCallsBy(pop.getFunctionCalls()); } // System.out.println("Fitnesscalls :" + this.population.getFunctionCalls()); - this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED, this.m_Optimizer.getPopulation()); - double plotValue = (this.m_Problem.getDoublePlotValue(this.m_Population)).doubleValue(); - if (this.m_Show) { - this.m_Plot.setConnectedPoint(this.m_Population.getFunctionCalls(), plotValue, 0); + this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED, this.optimizer.getPopulation()); + double plotValue = (this.optimizationProblem.getDoublePlotValue(this.population)).doubleValue(); + if (this.show) { + this.plot.setConnectedPoint(this.population.getFunctionCalls(), plotValue, 0); } // now they are synchronized - this.m_Migration.migrate(this.m_Islands); + this.migration.migrate(this.islands); } /** @@ -296,14 +296,14 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I */ @Override public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { - this.m_Listener = ea; + this.populationChangedEventListener = ea; } @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener == ea) { - m_Listener = null; + if (populationChangedEventListener == ea) { + populationChangedEventListener = null; return true; } else { return false; @@ -314,8 +314,8 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I * Something has changed */ protected void firePropertyChangedEvent(String name, Population population) { - if (this.m_Listener != null) { - this.m_Listener.registerPopulationStateChanged(this, name); + if (this.populationChangedEventListener != null) { + this.populationChangedEventListener.registerPopulationStateChanged(this, name); } } @@ -326,13 +326,13 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I */ @Override public void setProblem(InterfaceOptimizationProblem problem) { - this.m_Problem = problem; - this.m_Optimizer.setProblem(problem); + this.optimizationProblem = problem; + this.optimizer.setProblem(problem); } @Override public InterfaceOptimizationProblem getProblem() { - return this.m_Problem; + return this.optimizationProblem; } /** @@ -346,18 +346,18 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I String result = ""; result += "Island Model Evolutionary Algorithm:\n"; result += "Using:\n"; - result += " Migration Strategy = " + this.m_Migration.getClass().toString() + "\n"; - result += " Migration rate = " + this.m_MigrationRate + "\n"; - result += " Local only = " + this.m_localOnly + "\n"; + result += " Migration Strategy = " + this.migration.getClass().toString() + "\n"; + result += " Migration rate = " + this.migrationRate + "\n"; + result += " Local only = " + this.numLocalOnly + "\n"; result += " Het. Problems = " + this.heterogeneousProblems + "\n"; if (this.heterogeneousProblems) { result += " Heterogenuous Optimizers: \n"; - for (int i = 0; i < this.m_Islands.length; i++) { - result += this.m_Islands[i].getStringRepresentation() + "\n"; + for (int i = 0; i < this.islands.length; i++) { + result += this.islands[i].getStringRepresentation() + "\n"; } } else { - result += " Homogeneous Optimizer = " + this.m_Optimizer.getClass().toString() + "\n"; - result += this.m_Optimizer.getStringRepresentation() + "\n"; + result += " Homogeneous Optimizer = " + this.optimizer.getClass().toString() + "\n"; + result += this.optimizer.getStringRepresentation() + "\n"; } //result += "=> The Optimization Problem: "; //result += this.problem.getStringRepresentationForProblem(this) +"\n"; @@ -374,14 +374,14 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I // @todo die ServerStarter muss ich noch hin kriegen // @todo Wichtig ich brauche den eva2.tools.jproxy.RMIServer! IslandModelEA imea = new IslandModelEA(); - imea.m_Show = true; - imea.m_localOnly = false; + imea.show = true; + imea.numLocalOnly = false; if (false) { - imea.m_Optimizer = new MultiObjectiveEA(); - ((MultiObjectiveEA) imea.m_Optimizer).setArchiveSize(25); - ((MultiObjectiveEA) imea.m_Optimizer).getPopulation().setTargetSize(50); - imea.m_Problem = new TF1Problem(); - ((TF1Problem) imea.m_Problem).setEAIndividual(new ESIndividualDoubleData()); + imea.optimizer = new MultiObjectiveEA(); + ((MultiObjectiveEA) imea.optimizer).setArchiveSize(25); + ((MultiObjectiveEA) imea.optimizer).getPopulation().setTargetSize(50); + imea.optimizationProblem = new TF1Problem(); + ((TF1Problem) imea.optimizationProblem).setEAIndividual(new ESIndividualDoubleData()); // ((TF1Problem)imea.problem).setEAIndividual(new ESIndividualDoubleData()); // imea.problem = new TFPortfolioSelectionProblem(); // ((TFPortfolioSelectionProblem)imea.problem).setEAIndividual(new ESIndividualDoubleData()); @@ -389,23 +389,23 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I MOClusteringSeparation c = new MOClusteringSeparation(); c.getKMeans().setUseSearchSpace(false); c.setUseConstraints(true); - c.m_Debug = true; - imea.m_Migration = c; + c.debug = true; + imea.migration = c; } if (false) { MOConeSeparation c = new MOConeSeparation(); c.setUseConstraints(true); - c.m_Debug = true; - imea.m_Migration = c; + c.debug = true; + imea.migration = c; } if (true) { - imea.m_Migration = new MOBestMigration(); + imea.migration = new MOBestMigration(); } } else { - imea.m_Problem = new F8Problem(); - ((F1Problem) imea.m_Problem).setEAIndividual(new ESIndividualDoubleData()); + imea.optimizationProblem = new F8Problem(); + ((F1Problem) imea.optimizationProblem).setEAIndividual(new ESIndividualDoubleData()); } - imea.m_MigrationRate = 15; + imea.migrationRate = 15; imea.init(); while (imea.getPopulation().getFunctionCalls() < 25000) { imea.optimize(); @@ -421,12 +421,12 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I */ @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.identifier = name; } @Override public String getIdentifier() { - return this.m_Identifier; + return this.identifier; } /** @@ -435,7 +435,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I * @return An array of optimizers */ public InterfaceOptimizer[] getOptimizers() { - return this.m_Islands; + return this.islands; } /** @@ -464,14 +464,11 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I InterfaceOptimizer opt = (InterfaceOptimizer) source; int sourceID = new Integer(opt.getIdentifier()).intValue(); double cFCOpt = opt.getPopulation().getFunctionCalls(); - double plotValue = (this.m_Problem.getDoublePlotValue(opt.getPopulation())).doubleValue(); + double plotValue = (this.optimizationProblem.getDoublePlotValue(opt.getPopulation())).doubleValue(); - if (this.m_Show) { - this.m_Plot.setConnectedPoint(cFCOpt, plotValue, (sourceID + 1)); + if (this.show) { + this.plot.setConnectedPoint(cFCOpt, plotValue, (sourceID + 1)); } - - //System.out.println("Someone has finished, ("+this.m_Generation+"/"+this.m_Performed+")"); - //System.out.println(sourceID + " is at generation "+ opt.getPopulation().getGeneration() +" i'm at " +this.m_Generation); } @@ -494,10 +491,10 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I // TODO Deactivated from GUI because the current implementation does not really parallelize on a multicore. // Instead, the new direct problem parallelization can be used. // public boolean isLocalOnly() { -// return this.m_localOnly; +// return this.numLocalOnly; // } public void setLocalOnly(boolean b) { - this.m_localOnly = b; + this.numLocalOnly = b; } public String localOnlyTipText() { @@ -510,12 +507,12 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I * @return The current optimzation mode */ public boolean getShow() { - return this.m_Show; + return this.show; } public void setShow(boolean b) { - this.m_Show = b; - this.m_LogLocalChanges = b; + this.show = b; + this.logLocalChanges = b; } public String showTipText() { @@ -528,11 +525,11 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I * @return The current optimizing method */ public InterfaceOptimizer getOptimizer() { - return this.m_Optimizer; + return this.optimizer; } public void setOptimizer(InterfaceOptimizer b) { - this.m_Optimizer = b; + this.optimizer = b; } public String optimizerTipText() { @@ -545,11 +542,11 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I * @return The current migration strategy */ public InterfaceMigration getMigrationStrategy() { - return this.m_Migration; + return this.migration; } public void setMigrationStrategy(InterfaceMigration b) { - this.m_Migration = b; + this.migration = b; } public String migrationStrategyTipText() { @@ -562,11 +559,11 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I * @return The current migration rate */ public int getMigrationRate() { - return this.m_MigrationRate; + return this.migrationRate; } public void setMigrationRate(int b) { - this.m_MigrationRate = b; + this.migrationRate = b; } public String migrationRateTipText() { @@ -586,13 +583,13 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I */ @Override public Population getPopulation() { - return this.m_Population; + return this.population; } @Override public void setPopulation(Population pop) { // @todo Jetzt m�sste ich die pop auch auf die Rechner verteilen... - this.m_Population = pop; + this.population = pop; } public String populationTipText() { @@ -611,7 +608,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I */ public void setNumberLocalCPUs(int n) { if (n >= 1) { - this.m_numLocalCPUs = n; + this.numLocalCPUs = n; } else { System.err.println("Number of CPUs must be at least 1!"); } diff --git a/src/eva2/optimization/strategies/LTGA.java b/src/eva2/optimization/strategies/LTGA.java index b688c031..d9d55e02 100644 --- a/src/eva2/optimization/strategies/LTGA.java +++ b/src/eva2/optimization/strategies/LTGA.java @@ -27,7 +27,7 @@ public class LTGA implements InterfaceOptimizer, java.io.Serializable, Interface private static final Logger LOGGER = Logger.getLogger(LTGA.class.getName()); transient private InterfacePopulationChangedEventListener populationChangedEventListener = null; - private String m_Identifier = "LTGA"; + private String identifier = "LTGA"; private int probDim = 8; private int fitCrit = -1; private int popSize = 50; @@ -42,7 +42,7 @@ public class LTGA implements InterfaceOptimizer, java.io.Serializable, Interface public LTGA(LTGA l) { this.populationChangedEventListener = l.populationChangedEventListener; - this.m_Identifier = l.m_Identifier; + this.identifier = l.identifier; this.probDim = l.probDim; this.popSize = l.popSize; this.population = (Population) l.population.clone(); @@ -328,12 +328,12 @@ public class LTGA implements InterfaceOptimizer, java.io.Serializable, Interface @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.identifier = name; } @Override public String getIdentifier() { - return this.m_Identifier; + return this.identifier; } @Override diff --git a/src/eva2/optimization/strategies/MLTGA.java b/src/eva2/optimization/strategies/MLTGA.java index f7bf3f32..117c9e83 100644 --- a/src/eva2/optimization/strategies/MLTGA.java +++ b/src/eva2/optimization/strategies/MLTGA.java @@ -24,8 +24,8 @@ import java.util.logging.Logger; public class MLTGA implements InterfaceOptimizer, java.io.Serializable, InterfacePopulationChangedEventListener { private static final Logger LOGGER = Logger.getLogger(MLTGA.class.getName()); - transient private InterfacePopulationChangedEventListener m_Listener = null; - private String m_Identifier = "LTGA"; + transient private InterfacePopulationChangedEventListener populationChangedEventListener = null; + private String identifier = "MLTGA"; private int probDim = 8; private int fitCrit = -1; private int popSize = 50; @@ -39,8 +39,8 @@ public class MLTGA implements InterfaceOptimizer, java.io.Serializable, Interfac } public MLTGA(MLTGA l) { - this.m_Listener = l.m_Listener; - this.m_Identifier = l.m_Identifier; + this.populationChangedEventListener = l.populationChangedEventListener; + this.identifier = l.identifier; this.probDim = l.probDim; this.popSize = l.popSize; this.population = (Population) l.population.clone(); @@ -65,15 +65,15 @@ public class MLTGA implements InterfaceOptimizer, java.io.Serializable, Interfac @Override public void addPopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - this.m_Listener = ea; + this.populationChangedEventListener = ea; } @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener == ea) { - m_Listener = null; + if (populationChangedEventListener == ea) { + populationChangedEventListener = null; return true; } else { return false; @@ -287,8 +287,8 @@ public class MLTGA implements InterfaceOptimizer, java.io.Serializable, Interfac * Something has changed */ protected void firePropertyChangedEvent(String name) { - if (this.m_Listener != null) { - this.m_Listener.registerPopulationStateChanged(this, name); + if (this.populationChangedEventListener != null) { + this.populationChangedEventListener.registerPopulationStateChanged(this, name); } } @@ -309,12 +309,12 @@ public class MLTGA implements InterfaceOptimizer, java.io.Serializable, Interfac @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.identifier = name; } @Override public String getIdentifier() { - return this.m_Identifier; + return this.identifier; } @Override diff --git a/src/eva2/optimization/strategies/MemeticAlgorithm.java b/src/eva2/optimization/strategies/MemeticAlgorithm.java index 397c2afa..74e642d7 100644 --- a/src/eva2/optimization/strategies/MemeticAlgorithm.java +++ b/src/eva2/optimization/strategies/MemeticAlgorithm.java @@ -19,7 +19,6 @@ import java.util.Hashtable; * be applied to problems which implement the InterfaceLocalSearchable else the * local search will not be activated at all. * - * @version 1.0 */ @Description("This is a basic generational Memetic Algorithm. Local search steps are performed on a selected subset " + "of individuals after certain numbers of global search iterations. Note " @@ -38,21 +37,21 @@ public class MemeticAlgorithm implements InterfaceOptimizer, // int counter = 0; !? // int maxfunctioncalls = 1000; !? private boolean TRACE = false; - private String m_Identifier = ""; - private InterfaceOptimizationProblem m_Problem = new F1Problem(); - private InterfaceOptimizer m_GlobalOptimizer = new GeneticAlgorithm(); + private String identifier = ""; + private InterfaceOptimizationProblem optimizationProblem = new F1Problem(); + private InterfaceOptimizer globalOptimizer = new GeneticAlgorithm(); private InterfaceSelection selectorPlug = new SelectBestIndividuals(); - transient private InterfacePopulationChangedEventListener m_Listener; + transient private InterfacePopulationChangedEventListener populationChangedEventListener; public MemeticAlgorithm() { } public MemeticAlgorithm(MemeticAlgorithm a) { // this.population = (Population)a.population.clone(); - this.m_Problem = (InterfaceLocalSearchable) a.m_Problem.clone(); - this.m_GlobalOptimizer = (InterfaceOptimizer) a.m_GlobalOptimizer; + this.optimizationProblem = (InterfaceLocalSearchable) a.optimizationProblem.clone(); + this.globalOptimizer = (InterfaceOptimizer) a.globalOptimizer; this.selectorPlug = (InterfaceSelection) a.selectorPlug; - this.m_Identifier = a.m_Identifier; + this.identifier = a.identifier; this.localSearchSteps = a.localSearchSteps; this.subsetsize = a.subsetsize; this.globalSearchIterations = a.globalSearchIterations; @@ -69,7 +68,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer, this.setPopulation((Population) pop.clone()); if (reset) { this.getPopulation().init(); - this.m_Problem.evaluate(this.getPopulation()); + this.optimizationProblem.evaluate(this.getPopulation()); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } } @@ -77,9 +76,9 @@ public class MemeticAlgorithm implements InterfaceOptimizer, @Override public void init() { // counter = 0; - this.m_GlobalOptimizer.setProblem(this.m_Problem); - this.m_GlobalOptimizer.init(); - this.evaluatePopulation(this.m_GlobalOptimizer.getPopulation()); + this.globalOptimizer.setProblem(this.optimizationProblem); + this.globalOptimizer.init(); + this.evaluatePopulation(this.globalOptimizer.getPopulation()); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } @@ -89,7 +88,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer, * @param population The population that is to be evaluated */ private void evaluatePopulation(Population population) { - this.m_Problem.evaluate(population); + this.optimizationProblem.evaluate(population); population.incrGeneration(); } @@ -99,17 +98,17 @@ public class MemeticAlgorithm implements InterfaceOptimizer, if (TRACE) { System.out.println("global search"); } - this.m_GlobalOptimizer.optimize(); + this.globalOptimizer.optimize(); - if ((globalSearchIterations > 0) && (((this.m_GlobalOptimizer.getPopulation().getGeneration() % this.globalSearchIterations) == 0)) + if ((globalSearchIterations > 0) && (((this.globalOptimizer.getPopulation().getGeneration() % this.globalSearchIterations) == 0)) && (this.localSearchSteps > 0) - && (this.m_Problem instanceof InterfaceLocalSearchable)) { + && (this.optimizationProblem instanceof InterfaceLocalSearchable)) { // here the local search is performed if (TRACE) { System.out.println("Performing local search on " + subsetsize + " individuals."); } - Population gop = this.m_GlobalOptimizer.getPopulation(); + Population gop = this.globalOptimizer.getPopulation(); Population subset = selectorPlug.selectFrom(gop, subsetsize); Population subsetclone = new Population(); for (int i = 0; i < subset.size(); i++) { @@ -129,7 +128,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer, } // int dosearchsteps = this.localSearchSteps; - double cost = ((InterfaceLocalSearchable) this.m_Problem) + double cost = ((InterfaceLocalSearchable) this.optimizationProblem) .getLocalSearchStepFunctionCallEquivalent(); // int calls = gop.getFunctionCalls() + (int) Math.round(localSearchSteps // * cost * subset.size()); @@ -143,9 +142,9 @@ public class MemeticAlgorithm implements InterfaceOptimizer, // stopit = true; // } for (int i = 0; i < localSearchSteps; i++) { - ((InterfaceLocalSearchable) this.m_Problem).doLocalSearch(subsetclone); + ((InterfaceLocalSearchable) this.optimizationProblem).doLocalSearch(subsetclone); } - this.m_Problem.evaluate(subsetclone); + this.optimizationProblem.evaluate(subsetclone); if (this.lamarckism) { gop.removeAll(subset); gop.addPopulation(subsetclone); @@ -174,7 +173,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer, if (TRACE) { System.out.println("function calls" - + this.m_GlobalOptimizer.getPopulation().getFunctionCalls()); + + this.globalOptimizer.getPopulation().getFunctionCalls()); } this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } @@ -187,14 +186,14 @@ public class MemeticAlgorithm implements InterfaceOptimizer, @Override public void addPopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - this.m_Listener = ea; + this.populationChangedEventListener = ea; } @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener == ea) { - m_Listener = null; + if (populationChangedEventListener == ea) { + populationChangedEventListener = null; return true; } else { return false; @@ -205,11 +204,11 @@ public class MemeticAlgorithm implements InterfaceOptimizer, * Something has changed */ protected void firePropertyChangedEvent(String name) { - if (this.m_Listener != null) { + if (this.populationChangedEventListener != null) { if (TRACE) { System.out.println("firePropertyChangedEvent MA"); } - this.m_Listener.registerPopulationStateChanged(this, name); + this.populationChangedEventListener.registerPopulationStateChanged(this, name); } } @@ -220,13 +219,13 @@ public class MemeticAlgorithm implements InterfaceOptimizer, */ @Override public void setProblem(InterfaceOptimizationProblem problem) { - this.m_Problem = problem; - this.m_GlobalOptimizer.setProblem(this.m_Problem); + this.optimizationProblem = problem; + this.globalOptimizer.setProblem(this.optimizationProblem); } @Override public InterfaceOptimizationProblem getProblem() { - return this.m_Problem; + return this.optimizationProblem; } /** @@ -240,8 +239,8 @@ public class MemeticAlgorithm implements InterfaceOptimizer, String result = ""; result += "Memetic Algorithm:\n"; result += "Optimization Problem: "; - result += this.m_Problem.getStringRepresentationForProblem(this) + "\n"; - result += this.m_GlobalOptimizer.getStringRepresentation(); + result += this.optimizationProblem.getStringRepresentationForProblem(this) + "\n"; + result += this.globalOptimizer.getStringRepresentation(); return result; } @@ -252,12 +251,12 @@ public class MemeticAlgorithm implements InterfaceOptimizer, */ @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.identifier = name; } @Override public String getIdentifier() { - return this.m_Identifier; + return this.identifier; } @@ -280,12 +279,12 @@ public class MemeticAlgorithm implements InterfaceOptimizer, */ @Override public Population getPopulation() { - return this.m_GlobalOptimizer.getPopulation(); + return this.globalOptimizer.getPopulation(); } @Override public void setPopulation(Population pop) { - this.m_GlobalOptimizer.setPopulation(pop); + this.globalOptimizer.setPopulation(pop); } public String populationTipText() { @@ -295,16 +294,16 @@ public class MemeticAlgorithm implements InterfaceOptimizer, /** * Choose the global optimization strategy to use * - * @param m_GlobalOptimizer + * @param globalOptimizer */ - public void setGlobalOptimizer(InterfaceOptimizer m_GlobalOptimizer) { - this.m_GlobalOptimizer = m_GlobalOptimizer; - this.m_GlobalOptimizer.setProblem(this.getProblem()); + public void setGlobalOptimizer(InterfaceOptimizer globalOptimizer) { + this.globalOptimizer = globalOptimizer; + this.globalOptimizer.setProblem(this.getProblem()); this.init(); } public InterfaceOptimizer getGlobalOptimizer() { - return m_GlobalOptimizer; + return globalOptimizer; } public String globalOptimizerTipText() { diff --git a/src/eva2/optimization/strategies/MultiObjectiveCMAES.java b/src/eva2/optimization/strategies/MultiObjectiveCMAES.java index 32e1f372..0eb1915f 100644 --- a/src/eva2/optimization/strategies/MultiObjectiveCMAES.java +++ b/src/eva2/optimization/strategies/MultiObjectiveCMAES.java @@ -40,21 +40,21 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { public boolean seen = false; } - private String m_Identifier = "MOCMAES"; - private Population m_Population; - private AbstractOptimizationProblem m_Problem; - transient private InterfacePopulationChangedEventListener m_Listener; - private int m_lambda = 1; - private int m_lambdamo = 1; + private String identifier = "MOCMAES"; + private Population population; + private AbstractOptimizationProblem optimizationProblem; + transient private InterfacePopulationChangedEventListener populationChangedEventListener; + private int lambda = 1; + private int lambdaMO = 1; public MultiObjectiveCMAES() { - m_Population = new Population(m_lambdamo); + population = new Population(lambdaMO); } public MultiObjectiveCMAES(MultiObjectiveCMAES a) { - m_Problem = (AbstractOptimizationProblem) a.m_Problem.clone(); - setPopulation((Population) a.m_Population.clone()); - m_lambda = a.m_lambda; + optimizationProblem = (AbstractOptimizationProblem) a.optimizationProblem.clone(); + setPopulation((Population) a.population.clone()); + lambda = a.lambda; } @Override @@ -76,7 +76,7 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { */ @Override public void setIdentifier(String name) { - m_Identifier = name; + identifier = name; } /* @@ -88,7 +88,7 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { */ @Override public void setProblem(InterfaceOptimizationProblem problem) { - m_Problem = (AbstractOptimizationProblem) problem; + optimizationProblem = (AbstractOptimizationProblem) problem; } /** @@ -99,7 +99,7 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { @Override public void addPopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - this.m_Listener = ea; + this.populationChangedEventListener = ea; } /* @@ -120,7 +120,7 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { */ @Override public String getIdentifier() { - return m_Identifier; + return identifier; } /* @@ -130,7 +130,7 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { */ @Override public String getName() { - return "(1+" + m_lambda + ") MO-CMA-ES"; + return "(1+" + lambda + ") MO-CMA-ES"; } /* @@ -140,7 +140,7 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { */ @Override public Population getPopulation() { - return m_Population; + return population; } /* @@ -150,7 +150,7 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { */ @Override public InterfaceOptimizationProblem getProblem() { - return m_Problem; + return optimizationProblem; } /* @@ -162,10 +162,10 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { @Override public String getStringRepresentation() { StringBuilder strB = new StringBuilder(200); - strB.append("(1+" + m_lambda + ") MO-CMA-ES:\nOptimization Problem: "); - strB.append(this.m_Problem.getStringRepresentationForProblem(this)); + strB.append("(1+" + lambda + ") MO-CMA-ES:\nOptimization Problem: "); + strB.append(this.optimizationProblem.getStringRepresentationForProblem(this)); strB.append("\n"); - strB.append(this.m_Population.getStringRepresentation()); + strB.append(this.population.getStringRepresentation()); return strB.toString(); } @@ -177,10 +177,10 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { @Override public void init() { // initByPopulation(population, true); - this.m_Population.setTargetSize(m_lambdamo); - this.m_Problem.initializePopulation(this.m_Population); + this.population.setTargetSize(lambdaMO); + this.optimizationProblem.initializePopulation(this.population); // children = new Population(population.size()); - this.evaluatePopulation(this.m_Population); + this.evaluatePopulation(this.population); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } @@ -196,8 +196,8 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { public void initByPopulation(Population pop, boolean reset) { setPopulation(pop); if (reset) { - m_Problem.initializePopulation(m_Population); - m_Problem.evaluate(m_Population); + optimizationProblem.initializePopulation(population); + optimizationProblem.evaluate(population); } } @@ -208,7 +208,7 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { * @param population The population that is to be evaluated */ private void evaluatePopulation(Population population) { - this.m_Problem.evaluate(population); + this.optimizationProblem.evaluate(population); } /* @@ -222,20 +222,20 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { HashMap SuccessCounterMap = new HashMap(); // Eltern markieren und f�r die Z�hlung vorbereiten - for (int j = 0; j < m_lambdamo && j < m_Population.size(); j++) { - m_Population.getEAIndividual(j).putData("Parent", - m_Population.getEAIndividual(j)); - SuccessCounterMap.put(m_Population.getEAIndividual(j).getIndyID(), + for (int j = 0; j < lambdaMO && j < population.size(); j++) { + population.getEAIndividual(j).putData("Parent", + population.getEAIndividual(j)); + SuccessCounterMap.put(population.getEAIndividual(j).getIndyID(), new CounterClass(0)); } // Kinder erzeugen - Population children = new Population(m_lambdamo * m_lambda); - children.setGeneration(m_Population.getGeneration()); + Population children = new Population(lambdaMO * lambda); + children.setGeneration(population.getGeneration()); for (int j = 0; j < children.getTargetSize(); j++) { - AbstractEAIndividual parent = m_Population.getEAIndividual(j - % m_lambdamo); + AbstractEAIndividual parent = population.getEAIndividual(j + % lambdaMO); AbstractEAIndividual indy = (AbstractEAIndividual) parent.clone(); indy.mutate(); indy.putData("Parent", parent); @@ -243,29 +243,29 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { } evaluatePopulation(children); - m_Population.addPopulation(children); + population.addPopulation(children); // Ranking ArchivingNSGAII dummyArchive = new ArchivingNSGAIISMeasure(); Population[] store = dummyArchive - .getNonDominatedSortedFronts(m_Population); - store = dummyArchive.getNonDominatedSortedFronts(m_Population); + .getNonDominatedSortedFronts(population); + store = dummyArchive.getNonDominatedSortedFronts(population); dummyArchive.calculateCrowdingDistance(store); // Vergleichen und den Successcounter hochz�hlen wenn wir besser als // unser Elter sind - for (int j = 0; j < m_Population.size(); j++) { - AbstractEAIndividual parent = (AbstractEAIndividual) m_Population + for (int j = 0; j < population.size(); j++) { + AbstractEAIndividual parent = (AbstractEAIndividual) population .getEAIndividual(j).getData("Parent"); - if (m_Population.getEAIndividual(j) != parent) { // Eltern nicht mit + if (population.getEAIndividual(j) != parent) { // Eltern nicht mit // sich selber // vergleichen int parentParetoLevel = ((Integer) parent .getData("ParetoLevel")).intValue(); double parentSMeasure = ((Double) parent.getData("HyperCube")) .doubleValue(); - int childParetoLevel = ((Integer) m_Population.getEAIndividual( + int childParetoLevel = ((Integer) population.getEAIndividual( j).getData("ParetoLevel")).intValue(); - double childSMeasure = ((Double) m_Population + double childSMeasure = ((Double) population .getEAIndividual(j).getData("HyperCube")).doubleValue(); if (childParetoLevel < parentParetoLevel || ((childParetoLevel == parentParetoLevel) && childSMeasure > parentSMeasure)) { @@ -278,17 +278,17 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { } // Selection - m_Population.clear(); + population.clear(); for (int i = 0; i < store.length; i++) { - if (m_Population.size() + store[i].size() <= m_lambdamo) { // Die + if (population.size() + store[i].size() <= lambdaMO) { // Die // Front // passt // noch // komplett - m_Population.addPopulation(store[i]); + population.addPopulation(store[i]); } else { // die besten aus der aktuellen Front heraussuchen bis voll - while (store[i].size() > 0 && m_Population.size() < m_lambdamo) { + while (store[i].size() > 0 && population.size() < lambdaMO) { AbstractEAIndividual indy = store[i].getEAIndividual(0); double bestMeasure = ((Double) indy.getData("HyperCube")) .doubleValue(); // TODO mal noch effizient machen @@ -302,7 +302,7 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { indy = store[i].getEAIndividual(j); } } - m_Population.add(indy); + population.add(indy); store[i].removeMember(indy); } } @@ -310,9 +310,9 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { } // Strategieparemeter updaten - for (int j = 0; j < m_Population.size(); j++) { + for (int j = 0; j < population.size(); j++) { - AbstractEAIndividual indy = m_Population.getEAIndividual(j); + AbstractEAIndividual indy = population.getEAIndividual(j); if (indy.getMutationOperator() instanceof MutateESCovarianceMatrixAdaptionPlus) { // Das // geht // nur @@ -329,7 +329,7 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { .getMutationOperator(); double rate = ((double) SuccessCounterMap.get(parent .getIndyID()).value) - / ((double) m_lambda); + / ((double) lambda); if (indy != parent) { muta.updateCovariance(); @@ -342,8 +342,8 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { children.getEAIndividual(j).putData("Parent", null); } - m_Population.incrFunctionCallsBy(children.size()); - m_Population.incrGeneration(); + population.incrFunctionCallsBy(children.size()); + population.incrGeneration(); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } @@ -370,9 +370,9 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { */ @Override public void setPopulation(Population pop) { - m_Population = pop; - m_Population.setNotifyEvalInterval(1); - m_lambdamo = m_Population.getTargetSize(); + population = pop; + population.setNotifyEvalInterval(1); + lambdaMO = population.getTargetSize(); } @@ -382,22 +382,22 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable { * @param name */ protected void firePropertyChangedEvent(String name) { - if (this.m_Listener != null) { - this.m_Listener.registerPopulationStateChanged(this, name); + if (this.populationChangedEventListener != null) { + this.populationChangedEventListener.registerPopulationStateChanged(this, name); } } public int getLambda() { - return m_lambda; + return lambda; } public void setLambda(int mLambda) { - m_lambda = mLambda; + lambda = mLambda; } /* - * public int getLambdaMo() { return m_lambdamo; } + * public int getLambdaMo() { return lambdaMO; } * - * public void setLambdaMo(int mLambda) { m_lambdamo = mLambda; } + * public void setLambdaMo(int mLambda) { lambdaMO = mLambda; } */ } diff --git a/src/eva2/optimization/strategies/MultiObjectiveEA.java b/src/eva2/optimization/strategies/MultiObjectiveEA.java index e42a7c4f..5ef254a0 100644 --- a/src/eva2/optimization/strategies/MultiObjectiveEA.java +++ b/src/eva2/optimization/strategies/MultiObjectiveEA.java @@ -30,24 +30,24 @@ import eva2.util.annotation.Description; @Description("This is a general Multi-objective Evolutionary Optimization Framework.") public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializable { - private InterfaceOptimizer m_Optimizer = new GeneticAlgorithm(); - private InterfaceArchiving m_Archiver = new ArchivingNSGAII(); - private InterfaceInformationRetrieval m_InformationRetrieval = new InformationRetrievalInserting(); - private InterfaceOptimizationProblem m_Problem = new FM0Problem(); - private String m_Identifier = ""; - transient private InterfacePopulationChangedEventListener m_Listener; + private InterfaceOptimizer optimizer = new GeneticAlgorithm(); + private InterfaceArchiving archiver = new ArchivingNSGAII(); + private InterfaceInformationRetrieval informationRetrieval = new InformationRetrievalInserting(); + private InterfaceOptimizationProblem optimizationProblem = new FM0Problem(); + private String identifier = ""; + transient private InterfacePopulationChangedEventListener populationChangedEventListener; public MultiObjectiveEA() { - this.m_Optimizer.getPopulation().setTargetSize(100); - ((GeneticAlgorithm) this.m_Optimizer).setParentSelection(new SelectMONonDominated()); - ((GeneticAlgorithm) this.m_Optimizer).setPartnerSelection(new SelectMONonDominated()); + this.optimizer.getPopulation().setTargetSize(100); + ((GeneticAlgorithm) this.optimizer).setParentSelection(new SelectMONonDominated()); + ((GeneticAlgorithm) this.optimizer).setPartnerSelection(new SelectMONonDominated()); } public MultiObjectiveEA(MultiObjectiveEA a) { - this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone(); - this.m_Optimizer = (InterfaceOptimizer) a.m_Optimizer.clone(); - this.m_Archiver = (InterfaceArchiving) a.m_Archiver.clone(); - this.m_InformationRetrieval = (InterfaceInformationRetrieval) a.m_InformationRetrieval.clone(); + this.optimizationProblem = (InterfaceOptimizationProblem) a.optimizationProblem.clone(); + this.optimizer = (InterfaceOptimizer) a.optimizer.clone(); + this.archiver = (InterfaceArchiving) a.archiver.clone(); + this.informationRetrieval = (InterfaceInformationRetrieval) a.informationRetrieval.clone(); } public MultiObjectiveEA(InterfaceOptimizer subOpt, InterfaceArchiving archiving, int archiveSize, @@ -66,8 +66,8 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl @Override public void init() { - this.m_Optimizer.init(); - this.m_Archiver.addElementsToArchive(this.m_Optimizer.getPopulation()); + this.optimizer.init(); + this.archiver.addElementsToArchive(this.optimizer.getPopulation()); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } @@ -79,8 +79,8 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl */ @Override public void initByPopulation(Population pop, boolean reset) { - this.m_Optimizer.initByPopulation(pop, reset); - this.m_Archiver.addElementsToArchive(this.m_Optimizer.getPopulation()); + this.optimizer.initByPopulation(pop, reset); + this.archiver.addElementsToArchive(this.optimizer.getPopulation()); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } @@ -91,11 +91,11 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl public void optimize() { // double[][] may = this.showMay(this.optimizer.getPopulation()); // This is in total compliance with Koch's framework nice isn't it? - this.m_Optimizer.optimize(); + this.optimizer.optimize(); // now comes all the multiobjective optimization stuff // This is the Environment Selection - this.m_Archiver.addElementsToArchive(this.m_Optimizer.getPopulation()); - //if (true) this.m_Archiver.plotArchive(this.optimizer.getPopulation()); + this.archiver.addElementsToArchive(this.optimizer.getPopulation()); + //if (true) this.archiver.plotArchive(this.optimizer.getPopulation()); // if (false) { // int popSize = this.optimizer.getPopulation().size(); // int archiveSize = this.optimizer.getPopulation().getArchive().size(); @@ -112,7 +112,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl // The InformationRetrieval will choose from the archive and the current population // the population from which in the next generation the parents will be selected. - this.m_InformationRetrieval.retrieveInformationFrom(this.m_Optimizer.getPopulation()); + this.informationRetrieval.retrieveInformationFrom(this.optimizer.getPopulation()); // double[][] mayday = this.showMay(this.optimizer.getPopulation()); // if ((mayday[0][0] > may[0][0]) || (mayday[1][1] > may[1][1])) { @@ -157,14 +157,14 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl @Override public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { - this.m_Listener = ea; + this.populationChangedEventListener = ea; } @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener == ea) { - m_Listener = null; + if (populationChangedEventListener == ea) { + populationChangedEventListener = null; return true; } else { return false; @@ -172,8 +172,8 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl } protected void firePropertyChangedEvent(String name) { - if (this.m_Listener != null) { - this.m_Listener.registerPopulationStateChanged(this, name); + if (this.populationChangedEventListener != null) { + this.populationChangedEventListener.registerPopulationStateChanged(this, name); } } @@ -184,13 +184,13 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl */ @Override public void setProblem(InterfaceOptimizationProblem problem) { - this.m_Problem = problem; - this.m_Optimizer.setProblem(problem); + this.optimizationProblem = problem; + this.optimizer.setProblem(problem); } @Override public InterfaceOptimizationProblem getProblem() { - return this.m_Problem; + return this.optimizationProblem; } /** @@ -204,11 +204,11 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl String result = ""; result += "Multi-Objective Evolutionary Algorithm:\n"; result += "Using:\n"; - result += " Archiving Strategy = " + this.m_Archiver.getClass().toString() + "\n"; - result += " Information Retrieval = " + this.m_InformationRetrieval.getClass().toString() + "\n"; + result += " Archiving Strategy = " + this.archiver.getClass().toString() + "\n"; + result += " Information Retrieval = " + this.informationRetrieval.getClass().toString() + "\n"; result += " Information Retrieval = " + this.getClass().toString() + "\n"; - result += " Optimizer = " + this.m_Optimizer.getClass().toString() + "\n"; - result += this.m_Optimizer.getStringRepresentation() + "\n"; + result += " Optimizer = " + this.optimizer.getClass().toString() + "\n"; + result += this.optimizer.getStringRepresentation() + "\n"; //result += "=> The Optimization Problem: "; //result += this.problem.getStringRepresentationForProblem(this) +"\n"; //result += this.population.getStringRepresentation(); @@ -222,12 +222,12 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl */ @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.identifier = name; } @Override public String getIdentifier() { - return this.m_Identifier; + return this.identifier; } /** @@ -249,12 +249,12 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl */ @Override public Population getPopulation() { - return this.m_Optimizer.getPopulation(); + return this.optimizer.getPopulation(); } @Override public void setPopulation(Population pop) { - this.m_Optimizer.setPopulation(pop); + this.optimizer.setPopulation(pop); } public String populationTipText() { @@ -272,11 +272,11 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl * @return The current optimizing method */ public InterfaceOptimizer getOptimizer() { - return this.m_Optimizer; + return this.optimizer; } public void setOptimizer(InterfaceOptimizer b) { - this.m_Optimizer = b; + this.optimizer = b; } public String optimizerTipText() { @@ -289,11 +289,11 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl * @return The current optimizing method */ public InterfaceArchiving getArchivingStrategy() { - return this.m_Archiver; + return this.archiver; } public void setArchivingStrategy(InterfaceArchiving b) { - this.m_Archiver = b; + this.archiver = b; } public String archivingStrategyTipText() { @@ -307,11 +307,11 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl * @return The current optimizing method */ public InterfaceInformationRetrieval getInformationRetrieval() { - return this.m_InformationRetrieval; + return this.informationRetrieval; } public void setInformationRetrieval(InterfaceInformationRetrieval b) { - this.m_InformationRetrieval = b; + this.informationRetrieval = b; } public String informationRetrievalTipText() { @@ -324,19 +324,19 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl * @return The current optimizing method */ public int getArchiveSize() { - Population archive = this.m_Optimizer.getPopulation().getArchive(); + Population archive = this.optimizer.getPopulation().getArchive(); if (archive == null) { archive = new Population(); - this.m_Optimizer.getPopulation().SetArchive(archive); + this.optimizer.getPopulation().SetArchive(archive); } return archive.getTargetSize(); } public void setArchiveSize(int b) { - Population archive = this.m_Optimizer.getPopulation().getArchive(); + Population archive = this.optimizer.getPopulation().getArchive(); if (archive == null) { archive = new Population(); - this.m_Optimizer.getPopulation().SetArchive(archive); + this.optimizer.getPopulation().SetArchive(archive); } archive.setTargetSize(b); } diff --git a/src/eva2/optimization/strategies/NelderMeadSimplex.java b/src/eva2/optimization/strategies/NelderMeadSimplex.java index 3ae77d9a..0551506d 100644 --- a/src/eva2/optimization/strategies/NelderMeadSimplex.java +++ b/src/eva2/optimization/strategies/NelderMeadSimplex.java @@ -28,10 +28,10 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte // simulating the generational cycle. Set rather small (eg 5) for use as local search, higher for global search (eg 50) private int generationCycle = 50; private int fitIndex = 0; // choose criterion for multi objective functions - private Population m_Population; - private AbstractOptimizationProblem m_Problem; - private transient Vector m_Listener; - private String m_Identifier = "NelderMeadSimplex"; + private Population population; + private AbstractOptimizationProblem optimizationProblem; + private transient Vector populationChangedEventListeners; + private String identifier = "NelderMeadSimplex"; private boolean checkConstraints = true; public NelderMeadSimplex() { @@ -44,11 +44,11 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte } public NelderMeadSimplex(NelderMeadSimplex a) { - m_Problem = (AbstractOptimizationProblem) a.m_Problem.clone(); - setPopulation((Population) a.m_Population.clone()); + optimizationProblem = (AbstractOptimizationProblem) a.optimizationProblem.clone(); + setPopulation((Population) a.population.clone()); populationSize = a.populationSize; generationCycle = a.generationCycle; - m_Identifier = a.m_Identifier; + identifier = a.identifier; } @Override @@ -58,17 +58,17 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte @Override public void setIdentifier(String name) { - m_Identifier = name; + identifier = name; } @Override public void setProblem(InterfaceOptimizationProblem problem) { - m_Problem = (AbstractOptimizationProblem) problem; + optimizationProblem = (AbstractOptimizationProblem) problem; } public boolean setProblemAndPopSize(InterfaceOptimizationProblem problem) { setProblem(problem); - if (m_Problem instanceof AbstractProblemDouble) { + if (optimizationProblem instanceof AbstractProblemDouble) { setPopulationSize(((AbstractProblemDouble) problem).getProblemDimension() + 1); return true; } else { @@ -85,11 +85,11 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte public void addPopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { if (ea != null) { - if (m_Listener == null) { - m_Listener = new Vector(); + if (populationChangedEventListeners == null) { + populationChangedEventListeners = new Vector(); } - if (!m_Listener.contains(ea)) { - m_Listener.add(ea); + if (!populationChangedEventListeners.contains(ea)) { + populationChangedEventListeners.add(ea); } } } @@ -97,10 +97,10 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener == null) { + if (populationChangedEventListeners == null) { return false; } else { - return m_Listener.remove(ea); + return populationChangedEventListeners.remove(ea); } } @@ -167,7 +167,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte // // problem.evaluate(reflectedInd); AbstractEAIndividual reflectedInd = createEvalIndy(bestpop, r); - this.m_Population.incrFunctionCalls(); + this.population.incrFunctionCalls(); if (firstIsBetter(best, reflectedInd) && firstIsBetter(reflectedInd, bestpop.getWorstEAIndividual(fitIndex))) { return reflectedInd; @@ -181,7 +181,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte } AbstractEAIndividual e_ind = createEvalIndy(bestpop, e); - this.m_Population.incrFunctionCalls(); + this.population.incrFunctionCalls(); if (firstIsBetter(e_ind, reflectedInd)) { //expandiertes ist besser als reflektiertes return e_ind; @@ -202,7 +202,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte // ((InterfaceDataTypeDouble)c_ind).setDoubleGenotype(c); // problem.evaluate(c_ind); AbstractEAIndividual c_ind = createEvalIndy(bestpop, c); - this.m_Population.incrFunctionCalls(); + this.population.incrFunctionCalls(); if (firstIsBetterEqual(c_ind, worst)) { return c_ind; } @@ -214,46 +214,46 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte AbstractEAIndividual e_ind = (AbstractEAIndividual) ((AbstractEAIndividual) pop.getIndividual(1)).clone(); ((InterfaceDataTypeDouble) e_ind).setDoubleGenotype(newGenotype); e_ind.resetConstraintViolation(); - m_Problem.evaluate(e_ind); + optimizationProblem.evaluate(e_ind); if (e_ind.getFitness(0) < 6000) { - m_Problem.evaluate(e_ind); + optimizationProblem.evaluate(e_ind); } return e_ind; } @Override public String getIdentifier() { - return m_Identifier; + return identifier; } @Override public String getName() { - return m_Identifier; + return identifier; } @Override public Population getPopulation() { - return m_Population; + return population; } @Override public InterfaceOptimizationProblem getProblem() { - return m_Problem; + return optimizationProblem; } @Override public String getStringRepresentation() { StringBuilder strB = new StringBuilder(200); strB.append("Nelder-Mead-Simplex Strategy:\nOptimization Problem: "); - strB.append(this.m_Problem.getStringRepresentationForProblem(this)); + strB.append(this.optimizationProblem.getStringRepresentationForProblem(this)); strB.append("\n"); - strB.append(this.m_Population.getStringRepresentation()); + strB.append(this.population.getStringRepresentation()); return strB.toString(); } @Override public void init() { - initByPopulation(m_Population, true); + initByPopulation(population, true); } @Override @@ -261,16 +261,16 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte setPopulation(pop); pop.addPopulationChangedEventListener(this); if (reset) { - m_Problem.initializePopulation(m_Population); - m_Problem.evaluate(m_Population); + optimizationProblem.initializePopulation(population); + optimizationProblem.evaluate(population); } // fireNextGenerationPerformed(); } private void fireNextGenerationPerformed() { - if (m_Listener != null) { - for (int i = 0; i < m_Listener.size(); i++) { - m_Listener.elementAt(i).registerPopulationStateChanged(this, Population.NEXT_GENERATION_PERFORMED); + if (populationChangedEventListeners != null) { + for (int i = 0; i < populationChangedEventListeners.size(); i++) { + populationChangedEventListeners.elementAt(i).registerPopulationStateChanged(this, Population.NEXT_GENERATION_PERFORMED); } } } @@ -279,11 +279,11 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte public void optimize() { // make at least as many calls as there are individuals within the population. // this simulates the generational loop expected by some other modules - int evalCntStart = m_Population.getFunctionCalls(); + int evalCntStart = population.getFunctionCalls(); int evalsDone = 0; - m_Problem.evaluatePopulationStart(m_Population); + optimizationProblem.evaluatePopulationStart(population); do { - AbstractEAIndividual ind = simplexStep(m_Population); + AbstractEAIndividual ind = simplexStep(population); if (ind != null) { //Verbesserung gefunden double[] x = ((InterfaceDataTypeDouble) ind).getDoubleData(); double[][] range = ((InterfaceDataTypeDouble) ind).getDoubleRange(); @@ -294,32 +294,32 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte // problem.evaluate(ind); // this.population.incrFunctionCalls(); } - m_Population.set(m_Population.getIndexOfWorstIndividualNoConstr(fitIndex), ind, fitIndex); + population.set(population.getIndexOfWorstIndividualNoConstr(fitIndex), ind, fitIndex); } else {//keine Verbesserung gefunden shrink!! - double[] u_1 = ((InterfaceDataTypeDouble) m_Population.getBestEAIndividual(fitIndex)).getDoubleData(); + double[] u_1 = ((InterfaceDataTypeDouble) population.getBestEAIndividual(fitIndex)).getDoubleData(); - for (int j = 0; j < m_Population.size(); j++) { - double[] c = ((InterfaceDataTypeDouble) m_Population.getEAIndividual(j)).getDoubleData(); + for (int j = 0; j < population.size(); j++) { + double[] c = ((InterfaceDataTypeDouble) population.getEAIndividual(j)).getDoubleData(); for (int i = 0; i < c.length; i++) { c[i] = 0.5 * c[i] + 0.5 * u_1[i]; } - ((InterfaceDataTypeDouble) m_Population.getEAIndividual(j)).setDoubleGenotype(c); + ((InterfaceDataTypeDouble) population.getEAIndividual(j)).setDoubleGenotype(c); // population.getEAIndividual(j).resetConstraintViolation(); // not a good idea because during evaluation, a stats update may be performed which mustnt see indies which are evaluated, but possible constraints have been reset. } - m_Problem.evaluate(m_Population); + optimizationProblem.evaluate(population); } - evalsDone = m_Population.getFunctionCalls() - evalCntStart; + evalsDone = population.getFunctionCalls() - evalCntStart; } while (evalsDone < generationCycle); - m_Problem.evaluatePopulationEnd(m_Population); - this.m_Population.incrGeneration(); + optimizationProblem.evaluatePopulationEnd(population); + this.population.incrGeneration(); } @Override public void setPopulation(Population pop) { - m_Population = pop; - m_Population.addPopulationChangedEventListener(this); - m_Population.setNotifyEvalInterval(populationSize); + population = pop; + population.addPopulationChangedEventListener(this); + population.setNotifyEvalInterval(populationSize); } @Override @@ -340,9 +340,9 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte */ public void setPopulationSize(int populationSize) { this.populationSize = populationSize; - if (m_Population != null) { - m_Population.setTargetSize(populationSize); - m_Population.setNotifyEvalInterval(m_Population.getTargetSize()); + if (population != null) { + population.setTargetSize(populationSize); + population.setNotifyEvalInterval(population.getTargetSize()); } } diff --git a/src/eva2/optimization/strategies/NichePSO.java b/src/eva2/optimization/strategies/NichePSO.java index 8e464988..e8e79b67 100644 --- a/src/eva2/optimization/strategies/NichePSO.java +++ b/src/eva2/optimization/strategies/NichePSO.java @@ -119,16 +119,16 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac protected InterfaceAbsorptionStrategy absorptionStrategy = new StandardAbsorptionStrategy(); protected InterfaceSubswarmCreationStrategy subswarmCreationStrategy = new StandardSubswarmCreationStrategy(); // the problem - protected InterfaceOptimizationProblem m_Problem = new FM0Problem(); + protected InterfaceOptimizationProblem optimizationProblem = new FM0Problem(); // only used by island model ? - protected String m_Identifier = ""; + protected String identifier = ""; // eventListener - transient protected InterfacePopulationChangedEventListener m_Listener; + transient protected InterfacePopulationChangedEventListener populationChangedEventListener; // for debugging: file containing the output transient protected BufferedWriter outputFile = null; // for debugging and plotting ----------------------------------------------- - transient protected TopoPlot m_TopologySwarm; - transient protected boolean m_shownextplot = false; + transient protected TopoPlot topoPlot; + transient protected boolean showNextPlot = false; transient protected boolean deactivationOccured = false; transient protected boolean mergingOccurd = false; transient protected boolean absorbtionOccurd = false; @@ -214,9 +214,9 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac this.absorptionStrategy = (InterfaceAbsorptionStrategy) a.absorptionStrategy.clone(); this.subswarmCreationStrategy = (InterfaceSubswarmCreationStrategy) a.subswarmCreationStrategy.clone(); - this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone(); + this.optimizationProblem = (InterfaceOptimizationProblem) a.optimizationProblem.clone(); - this.m_Identifier = a.m_Identifier; + this.identifier = a.identifier; } /** @@ -239,7 +239,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac protected void initMainSwarm() { // pass NichePSO parameter on to the mainswarmoptimzer setMainSwarmSize(mainSwarmSize); // (particles are initialized later via init) - getMainSwarm().setProblem(m_Problem); + getMainSwarm().setProblem(optimizationProblem); getMainSwarm().SetMaxAllowedSwarmRadius(maxAllowedSwarmRadius); getMainSwarm().getPopulation().setGeneration(0); @@ -267,7 +267,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac */ protected void initSubswarmOptimizerTemplate() { // pass on the parameters set via NichePSO (done in the analogous nichePSO-Setters as well -> no init() necessary) - getSubswarmOptimizerTemplate().setProblem(m_Problem); + getSubswarmOptimizerTemplate().setProblem(optimizationProblem); getSubswarmOptimizerTemplate().SetMaxAllowedSwarmRadius(maxAllowedSwarmRadius); // choose PSO-type for the subswarmoptimizer @@ -294,7 +294,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac public ParticleSubSwarmOptimization getNewSubSwarmOptimizer() { //initSubswarmOptimizerTemplate(); ParticleSubSwarmOptimization template = (ParticleSubSwarmOptimization) getSubswarmOptimizerTemplate().clone(); // this implicitely clones the problem but does not initialize it again... - template.setProblem(this.m_Problem); //... let all subswarms use the same correct initialised problem instance + template.setProblem(this.optimizationProblem); //... let all subswarms use the same correct initialised problem instance return template; } @@ -459,8 +459,8 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac } protected void doPlot() { - m_shownextplot = (deactivationOccured || mergingOccurd || absorbtionOccurd || creationOccurd);// repopoccurd || reinitoccurd); - if (this.getMainSwarm().getPopulation().getGeneration() % this.getShowCycle() == 0) {// || m_shownextplot){ + showNextPlot = (deactivationOccured || mergingOccurd || absorbtionOccurd || creationOccurd);// repopoccurd || reinitoccurd); + if (this.getMainSwarm().getPopulation().getGeneration() % this.getShowCycle() == 0) {// || showNextPlot){ // plot merging step if (mergingOccurd) { plotMainSwarm(false); @@ -476,7 +476,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac } } // plot main step - synchronized (m_TopologySwarm.getClass()) { + synchronized (topoPlot.getClass()) { plotMainSwarm(false); plotSubSwarms(); //plotAdditionalInfo(); @@ -494,7 +494,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac } //plotBoundStdDevInMainSwarm(0.5); - m_shownextplot = false; + showNextPlot = false; } } @@ -572,8 +572,8 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac this.borg.add((ParticleSubSwarmOptimization) borg.clone()); // for plotting only this.others.add((ParticleSubSwarmOptimization) others.clone()); // for plotting only mergingOccurd = true; - this.borgbest.add(borg.m_BestIndividual); // for plotting only - this.othersbest.add(others.m_BestIndividual); // for plotting only + this.borgbest.add(borg.bestIndividual); // for plotting only + this.othersbest.add(others.bestIndividual); // for plotting only } /** @@ -705,8 +705,8 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac * @tested Something has changed */ protected void firePropertyChangedEvent(String name) { - if (this.m_Listener != null) { - this.m_Listener.registerPopulationStateChanged(this, name); + if (this.populationChangedEventListener != null) { + this.populationChangedEventListener.registerPopulationStateChanged(this, name); } } @@ -718,14 +718,14 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac @Override public void addPopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - this.m_Listener = ea; + this.populationChangedEventListener = ea; } @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener == ea) { - m_Listener = null; + if (populationChangedEventListener == ea) { + populationChangedEventListener = null; return true; } else { return false; @@ -906,11 +906,11 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac // AbstractEAIndividual[] representatives = new AbstractEAIndividual[getSubSwarms().size()+1]; for (int i = 0; i < getSubSwarms().size(); ++i) { if (!onlyInactive || (!subSwarms.get(i).isActive())) { - representatives.add((AbstractEAIndividual) (subSwarms.get(i)).m_BestIndividual.clone()); + representatives.add((AbstractEAIndividual) (subSwarms.get(i)).bestIndividual.clone()); } } if (!onlyInactive && (getMainSwarm().getPopulation().size() != 0)) { - representatives.add((AbstractEAIndividual) getMainSwarm().m_BestIndividual.clone()); // assures at least one solution, even if no subswarm has been created + representatives.add((AbstractEAIndividual) getMainSwarm().bestIndividual.clone()); // assures at least one solution, even if no subswarm has been created } return representatives; } @@ -1275,7 +1275,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac */ @Override public InterfaceOptimizationProblem getProblem() { - return this.m_Problem; + return this.optimizationProblem; } /** @@ -1285,7 +1285,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac @Override public void setProblem(InterfaceOptimizationProblem problem) { // set member - this.m_Problem = problem; + this.optimizationProblem = problem; // pass on to the main- and subswarm optimizers getMainSwarm().setProblem(problem); for (int i = 0; i < getSubSwarms().size(); ++i) { @@ -1300,7 +1300,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac */ @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.identifier = name; } /** @@ -1309,7 +1309,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac */ @Override public String getIdentifier() { - return this.m_Identifier; + return this.identifier; } /** @@ -1456,7 +1456,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac * @tested emp */ public String getPerformanceAsString() { - if (!(m_Problem instanceof InterfaceMultimodalProblem)) { + if (!(optimizationProblem instanceof InterfaceMultimodalProblem)) { System.out.println("getPerformanceAsString: problem not instanceof InterfaceMultimodalProblem"); return ""; } @@ -1467,10 +1467,10 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac Population elitepop = getSubswarmRepresentatives(false); // use elite population to compute performance - if (m_Problem instanceof InterfaceMultimodalProblem) { - result += ((InterfaceMultimodalProblemKnown) m_Problem).getNumberOfFoundOptima(elitepop); - result += "(" + ((InterfaceMultimodalProblemKnown) m_Problem).getRealOptima().size() + ")\t"; - result += ((InterfaceMultimodalProblemKnown) m_Problem).getMaximumPeakRatio(elitepop) + "\t"; + if (optimizationProblem instanceof InterfaceMultimodalProblem) { + result += ((InterfaceMultimodalProblemKnown) optimizationProblem).getNumberOfFoundOptima(elitepop); + result += "(" + ((InterfaceMultimodalProblemKnown) optimizationProblem).getRealOptima().size() + ")\t"; + result += ((InterfaceMultimodalProblemKnown) optimizationProblem).getMaximumPeakRatio(elitepop) + "\t"; //boolean[] opts = ((InterfaceMultimodalProblem)problem).whichOptimaAreFound(elitepop); //result += "Optima:"; //for (int i = 0; i < opts.length; ++i){ @@ -1516,13 +1516,10 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac outputPath.mkdirs(); } - //String outputPath = getDirForCurrentExperiment()+"/NichePSO-LogFiles/"; - //OutputPath = OutputPath + dirForCurrentExperiment+"\\NichePSO-LogFiles\\"; - // file name SimpleDateFormat formatter = new SimpleDateFormat("yyyy.MM.dd'_'HH.mm.ss'_'E"); - String m_StartDate = formatter.format(new Date()); - String name = "NichePSO-LogFile" + "__" + m_StartDate + ".dat"; + String startDate = formatter.format(new Date()); + String name = "NichePSO-LogFile" + "__" + startDate + ".dat"; File f = new File(outputPath, name); // plattform independent representation... @@ -1634,10 +1631,10 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac double[] a = new double[2]; a[0] = 0.0; a[1] = 0.0; - this.m_TopologySwarm = new TopoPlot("NichePSO-MainSwarm", "x", "y", a, a); - this.m_TopologySwarm.setParams(60, 60); - if (m_Problem instanceof Interface2DBorderProblem) { - this.m_TopologySwarm.setTopology((Interface2DBorderProblem) this.m_Problem); + this.topoPlot = new TopoPlot("NichePSO-MainSwarm", "x", "y", a, a); + this.topoPlot.setParams(60, 60); + if (optimizationProblem instanceof Interface2DBorderProblem) { + this.topoPlot.setTopology((Interface2DBorderProblem) this.optimizationProblem); } // draws colored plot } @@ -1646,12 +1643,12 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac */ protected void cleanPlotSwarm() { // delete all previous points - DElement[] elements = this.m_TopologySwarm.getFunctionArea().getDElements(); + DElement[] elements = this.topoPlot.getFunctionArea().getDElements(); int lastIndex = elements.length - 1; DElement last = elements[lastIndex]; while (last instanceof DPointSet || last instanceof DPoint || last instanceof DPointIcon) { - this.m_TopologySwarm.getFunctionArea().removeDElement(last); -// elements = this.m_TopologySwarm.getFunctionArea().getDElements(); + this.topoPlot.getFunctionArea().removeDElement(last); +// elements = this.topoPlot.getFunctionArea().getDElements(); lastIndex--; last = elements[lastIndex]; } @@ -1678,23 +1675,8 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac DPointIcon icon = new Chart2DDPointIconText(id + ds); ((Chart2DDPointIconText) icon).setIcon(new Chart2DDPointIconCircle()); point.setIcon(icon); - this.m_TopologySwarm.getFunctionArea().addDElement(point); + this.topoPlot.getFunctionArea().addDElement(point); } - - //try to add further information to MainSwarm-Plot at once - /* popRep = new DPointSet(); - for (int i = 0; i < mainpop.size(); ++i){ - tmpIndy1 = (InterfaceDataTypeDouble)mainpop.get(i); - DPoint point = new DPoint(tmpIndy1.getDoubleData()[0], tmpIndy1.getDoubleData()[1]); - double[] da = (double[])(((AbstractEAIndividual)tmpIndy1).getData("StdDevKey"));//Math.round(100*((AbstractEAIndividual)tmpIndy1).getFitness(0))/(double)100; - double d = da[0]; - String ds = String.format("%6.2f", d); - DPointIcon icon = new Chart2DDPointIconText(ds); - ((Chart2DDPointIconText)icon).setIcon(new Chart2DDPointIconCircle()); - point.setIcon(icon); - popRep.addDPoint(point); - } - this.m_TopologyMainSwarm.plotArea.addDElement(popRep); */ } /** @@ -1712,7 +1694,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac DPointIcon icon = new Chart2DDPointIconText(ds); ((Chart2DDPointIconText) icon).setIcon(new Chart2DDPointIconCircle()); point.setIcon(icon); - this.m_TopologySwarm.getFunctionArea().addDElement(point); + this.topoPlot.getFunctionArea().addDElement(point); } /** @@ -1737,7 +1719,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac ((Chart2DDPointIconText) icon).setIcon(new Chart2DDPointIconCircle()); point.setIcon(icon); if (d < boundary) { - this.m_TopologySwarm.getFunctionArea().addDElement(point); + this.topoPlot.getFunctionArea().addDElement(point); } } } @@ -1754,7 +1736,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac DPointIcon icon = new Chart2DDPointIconText(text); ((Chart2DDPointIconText) icon).setIcon(new Chart2DDPointIconCircle()); point.setIcon(icon); - this.m_TopologySwarm.getFunctionArea().addDElement(point); + this.topoPlot.getFunctionArea().addDElement(point); } /** @@ -1768,14 +1750,14 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac DPointIcon icon = new Chart2DDPointIconText(text); ((Chart2DDPointIconText) icon).setIcon(new Chart2DDPointIconCircle()); point.setIcon(icon); - this.m_TopologySwarm.getFunctionArea().addDElement(point); + this.topoPlot.getFunctionArea().addDElement(point); } /** * @tested cleans the previous plot and plots the mainswarm as points */ protected void plotMainSwarm(boolean withIDs) { - if (this.m_Problem instanceof Interface2DBorderProblem) { + if (this.optimizationProblem instanceof Interface2DBorderProblem) { DPointSet popRep = new DPointSet(); InterfaceDataTypeDouble tmpIndy1; @@ -1787,7 +1769,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac tmpIndy1 = (InterfaceDataTypeDouble) mainpop.get(i); popRep.addDPoint(new DPoint(tmpIndy1.getDoubleData()[0], tmpIndy1.getDoubleData()[1])); } - this.m_TopologySwarm.getFunctionArea().addDElement(popRep); // time consuming + this.topoPlot.getFunctionArea().addDElement(popRep); // time consuming // identify every particle in the main swarm if (withIDs) { @@ -1821,10 +1803,10 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac for (int i = 0; i < this.getSubSwarms().size(); ++i) { ParticleSubSwarmOptimization currentsub = this.getSubSwarms().get(i); if (!currentsub.isActive() && plotInactive) { - plotCircleForIndy(currentsub.m_BestIndividual, String.valueOf(i) + "[I]"); + plotCircleForIndy(currentsub.bestIndividual, String.valueOf(i) + "[I]"); } if (currentsub.isActive() && plotActive) { - plotCircleForIndy(currentsub.m_BestIndividual, String.valueOf(i)); + plotCircleForIndy(currentsub.bestIndividual, String.valueOf(i)); } } } @@ -1848,7 +1830,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac protected void plotSwarmToMerge(ParticleSubSwarmOptimization swarm, int index) { InterfaceDataTypeDouble tmpIndy1; Population swarmpop = (Population) swarm.getPopulation(); - InterfaceDataTypeDouble best = (InterfaceDataTypeDouble) swarm.m_BestIndividual; + InterfaceDataTypeDouble best = (InterfaceDataTypeDouble) swarm.bestIndividual; DPointSet popRep = new DPointSet(); //...draw SubSwarm as points @@ -1857,7 +1839,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac tmpIndy1 = (InterfaceDataTypeDouble) swarmpop.get(j); popRep.addDPoint(new DPoint(tmpIndy1.getDoubleData()[0], tmpIndy1.getDoubleData()[1])); } - this.m_TopologySwarm.getFunctionArea().addDElement(popRep); // time consuming + this.topoPlot.getFunctionArea().addDElement(popRep); // time consuming //...draw circle for best if (!swarm.isActive()) { @@ -1874,7 +1856,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac popRep.addDPoint(new DPoint(tmpIndy1.getDoubleData()[0], tmpIndy1.getDoubleData()[1])); popRep.addDPoint(new DPoint(best.getDoubleData()[0], best.getDoubleData()[1])); } - this.m_TopologySwarm.getFunctionArea().addDElement(popRep); // time consuming + this.topoPlot.getFunctionArea().addDElement(popRep); // time consuming } @@ -1883,7 +1865,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac * individual */ protected void plotSubSwarms() { - if (this.m_Problem instanceof Interface2DBorderProblem) { + if (this.optimizationProblem instanceof Interface2DBorderProblem) { //DPointSet popRep = new DPointSet(); InterfaceDataTypeDouble tmpIndy1; @@ -1894,7 +1876,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac ParticleSubSwarmOptimization currentsubswarm = this.getSubSwarms().get(i); Population currentsubswarmpop = (Population) currentsubswarm.getPopulation(); //InterfaceDataTypeDouble best = (InterfaceDataTypeDouble)currentsubswarmpop.getBestIndividual(); - InterfaceDataTypeDouble best = (InterfaceDataTypeDouble) currentsubswarm.m_BestIndividual; + InterfaceDataTypeDouble best = (InterfaceDataTypeDouble) currentsubswarm.bestIndividual; DPointSet popRep = new DPointSet(); //...draw SubSwarm as points @@ -1903,7 +1885,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac tmpIndy1 = (InterfaceDataTypeDouble) currentsubswarmpop.get(j); popRep.addDPoint(new DPoint(tmpIndy1.getDoubleData()[0], tmpIndy1.getDoubleData()[1])); } - this.m_TopologySwarm.getFunctionArea().addDElement(popRep); // time consuming + this.topoPlot.getFunctionArea().addDElement(popRep); // time consuming //...draw circle for best if (!currentsubswarm.isActive()) { @@ -1932,7 +1914,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac popRep.addDPoint(new DPoint(tmpIndy1.getDoubleData()[0], tmpIndy1.getDoubleData()[1])); popRep.addDPoint(new DPoint(best.getDoubleData()[0], best.getDoubleData()[1])); } - this.m_TopologySwarm.getFunctionArea().addDElement(popRep); // time consuming + this.topoPlot.getFunctionArea().addDElement(popRep); // time consuming } } // endif } @@ -1985,7 +1967,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac // from deactivation and reinit if (deactivationOccured) { for (int i = 0; i < deactivatedSwarm.size(); ++i) { - plotCircleForIndy(deactivatedSwarm.get(i).m_BestIndividual, " deac"); + plotCircleForIndy(deactivatedSwarm.get(i).bestIndividual, " deac"); } // for (int i = 0; i < reinitedSwarm.size(); ++i){ // plotSwarm(reinitedSwarm.get(i)," reinit"); @@ -2029,7 +2011,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac DPointIcon icon = new Chart2DDPointIconText(""); ((Chart2DDPointIconText) icon).setIcon(new Chart2DDPointIconPoint()); point.setIcon(icon); - this.m_TopologySwarm.getFunctionArea().addDElement(point); + this.topoPlot.getFunctionArea().addDElement(point); //plot oldPos if (!(indy.getData("oldPosition") == null)) { @@ -2038,7 +2020,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac icon = new Chart2DDPointIconText(""); ((Chart2DDPointIconText) icon).setIcon(new Chart2DDPointIconCross()); point.setIcon(icon); - this.m_TopologySwarm.getFunctionArea().addDElement(point); + this.topoPlot.getFunctionArea().addDElement(point); } //plot personalBestPos @@ -2047,7 +2029,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac icon = new Chart2DDPointIconText(""); ((Chart2DDPointIconText) icon).setIcon(new Chart2DDPointIconCircle()); point.setIcon(icon); - this.m_TopologySwarm.getFunctionArea().addDElement(point); + this.topoPlot.getFunctionArea().addDElement(point); //plot neighbourBestPos double[] neighbourBestPos = (double[]) indy.getData("neighbourBestPos"); @@ -2055,7 +2037,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac icon = new Chart2DDPointIconText(""); ((Chart2DDPointIconText) icon).setIcon(new Chart2DDPointIconContent()); point.setIcon(icon); - this.m_TopologySwarm.getFunctionArea().addDElement(point); + this.topoPlot.getFunctionArea().addDElement(point); } // protected void saveCurrentPlotAsJPG(String filename){ @@ -2077,16 +2059,16 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac // // File f = new File(outputPath,name); // -// synchronized(m_TopologySwarm){ +// synchronized(topoPlot){ // try { -// JFrame frame = m_TopologySwarm.internalFrame; +// JFrame frame = topoPlot.internalFrame; // Robot robot = new Robot(); // Rectangle area; // area = frame.getBounds(); // BufferedImage bufferedImage = robot.createScreenCapture(area); // // // JFileChooser fc = new JFileChooser(); -// // if (fc.showSaveDialog(m_TopologySwarm.internalFrame) != JFileChooser.APPROVE_OPTION) return; +// // if (fc.showSaveDialog(topoPlot.internalFrame) != JFileChooser.APPROVE_OPTION) return; // System.out.println("Name " + f.getName()); // try { // FileOutputStream fos = new FileOutputStream(f); diff --git a/src/eva2/optimization/strategies/ParticleFilterOptimization.java b/src/eva2/optimization/strategies/ParticleFilterOptimization.java index eb12b1fc..6f87ab28 100644 --- a/src/eva2/optimization/strategies/ParticleFilterOptimization.java +++ b/src/eva2/optimization/strategies/ParticleFilterOptimization.java @@ -33,10 +33,10 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S * serialVersionUID */ private static final long serialVersionUID = 1L; - private Population m_Population = new Population(); - private InterfaceOptimizationProblem m_Problem = new F1Problem(); - private InterfaceSelection m_ParentSelection = new SelectParticleWheel(0.5); - private String m_Identifier = ""; + private Population population = new Population(); + private InterfaceOptimizationProblem optimizationProblem = new F1Problem(); + private InterfaceSelection parentSelection = new SelectParticleWheel(0.5); + private String identifier = ""; private boolean withShow = false; private double mutationSigma = 0.01; private double randomImmigrationQuota = 0.05; @@ -45,7 +45,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S private int popSize = 300; private int sleepTime = 0; transient private int indCount = 0; - transient private InterfacePopulationChangedEventListener m_Listener; + transient private InterfacePopulationChangedEventListener populationChangedEventListener; transient Plot myPlot = null; public static final boolean TRACE = false; @@ -60,20 +60,17 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S initialVelocity = vInit; randomImmigrationQuota = immiQuote; rotationDeg = rotDeg; - m_ParentSelection = new SelectParticleWheel(selScaling); + parentSelection = new SelectParticleWheel(selScaling); if (withShow) { setWithShow(true); } } public ParticleFilterOptimization(ParticleFilterOptimization a) { - this.m_Population = (Population) a.m_Population.clone(); - this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone(); - this.m_Identifier = a.m_Identifier; - //this.m_Plague = a.m_Plague; - //this.m_NumberOfPartners = a.m_NumberOfPartners; - //this.m_UseElitism = a.m_UseElitism; - this.m_ParentSelection = (InterfaceSelection) a.m_ParentSelection.clone(); + this.population = (Population) a.population.clone(); + this.optimizationProblem = (InterfaceOptimizationProblem) a.optimizationProblem.clone(); + this.identifier = a.identifier; + this.parentSelection = (InterfaceSelection) a.parentSelection.clone(); if (a.withShow) { setWithShow(true); } @@ -94,16 +91,16 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S //System.out.println("pops targ is " + population.getPopulationSize()); if (initialVelocity <= 0.) { - (((AbstractOptimizationProblem) m_Problem).getIndividualTemplate()).setMutationOperator(new MutateESFixedStepSize(mutationSigma)); + (((AbstractOptimizationProblem) optimizationProblem).getIndividualTemplate()).setMutationOperator(new MutateESFixedStepSize(mutationSigma)); } else { - (((AbstractOptimizationProblem) m_Problem).getIndividualTemplate()).setMutationOperator(new MutateESCorrVector(mutationSigma, initialVelocity, rotationDeg)); + (((AbstractOptimizationProblem) optimizationProblem).getIndividualTemplate()).setMutationOperator(new MutateESCorrVector(mutationSigma, initialVelocity, rotationDeg)); } - m_Population.setTargetSize(popSize); - this.m_Problem.initializePopulation(this.m_Population); + population.setTargetSize(popSize); + this.optimizationProblem.initializePopulation(this.population); setWithShow(withShow); - this.evaluatePopulation(this.m_Population); + this.evaluatePopulation(this.population); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } @@ -115,10 +112,10 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S */ @Override public void initByPopulation(Population pop, boolean reset) { - this.m_Population = (Population) pop.clone(); + this.population = (Population) pop.clone(); if (reset) { - this.m_Population.init(); - this.evaluatePopulation(this.m_Population); + this.population.init(); + this.evaluatePopulation(this.population); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } } @@ -129,7 +126,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S * @param population The population that is to be evaluated */ private Population evaluatePopulation(Population population) { - this.m_Problem.evaluate(population); + this.optimizationProblem.evaluate(population); population.incrGeneration(); return population; } @@ -141,24 +138,24 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S Population parents; boolean doImmigr = false; - this.m_ParentSelection.prepareSelection(pop); + this.parentSelection.prepareSelection(pop); // Generate a Population of Parents with Parantselectionmethod. // DONT forget cloning -> selection does only shallow copies! - int targetSize = this.m_Population.getTargetSize(); + int targetSize = this.population.getTargetSize(); if (randomImmigrationQuota > 0) { if (randomImmigrationQuota > 1.) { System.err.println("Error, invalid immigration quota!"); } else { - targetSize = (int) (this.m_Population.getTargetSize() * (1. - randomImmigrationQuota)); + targetSize = (int) (this.population.getTargetSize() * (1. - randomImmigrationQuota)); targetSize = Math.max(1, targetSize); // guarantee at least one to be selected - if (targetSize < this.m_Population.getTargetSize()) { + if (targetSize < this.population.getTargetSize()) { doImmigr = true; } } } - parents = (Population) (this.m_ParentSelection.selectFrom(pop, targetSize)).clone(); + parents = (Population) (this.parentSelection.selectFrom(pop, targetSize)).clone(); if (doImmigr) { // add immigrants @@ -238,7 +235,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S //AbstractEAIndividual elite; // resample using selection - nextGeneration = resample(m_Population); + nextGeneration = resample(population); if (sleepTime > 0) { try { @@ -253,10 +250,10 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S // predict step predict(nextGeneration); if (TRACE) { - System.out.println("Speed is " + BeanInspector.toString(ParticleSwarmOptimization.getPopulationVelSpeed(m_Population, 3, MutateESCorrVector.vectorKey, null, null)) + " popM " + BeanInspector.toString(m_Population.getPopulationMeasures(new EuclideanMetric()))); + System.out.println("Speed is " + BeanInspector.toString(ParticleSwarmOptimization.getPopulationVelSpeed(population, 3, MutateESCorrVector.vectorKey, null, null)) + " popM " + BeanInspector.toString(population.getPopulationMeasures(new EuclideanMetric()))); } - m_Population = evaluatePopulation(nextGeneration); + population = evaluatePopulation(nextGeneration); // collectStatistics(population); @@ -266,14 +263,14 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S @Override public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { - this.m_Listener = ea; + this.populationChangedEventListener = ea; } @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener == ea) { - m_Listener = null; + if (populationChangedEventListener == ea) { + populationChangedEventListener = null; return true; } else { return false; @@ -281,8 +278,8 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S } protected void firePropertyChangedEvent(String name) { - if (this.m_Listener != null) { - this.m_Listener.registerPopulationStateChanged(this, name); + if (this.populationChangedEventListener != null) { + this.populationChangedEventListener.registerPopulationStateChanged(this, name); } } @@ -293,7 +290,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S */ @Override public void setProblem(InterfaceOptimizationProblem problem) { - this.m_Problem = problem; + this.optimizationProblem = problem; if (problem instanceof AbstractOptimizationProblem) { ((AbstractOptimizationProblem) problem).informAboutOptimizer(this); } @@ -301,7 +298,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S @Override public InterfaceOptimizationProblem getProblem() { - return this.m_Problem; + return this.optimizationProblem; } /** @@ -314,9 +311,9 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S public String getStringRepresentation() { StringBuilder strB = new StringBuilder(200); strB.append("Particle Filter:\nOptimization Problem: "); - strB.append(this.m_Problem.getStringRepresentationForProblem(this)); + strB.append(this.optimizationProblem.getStringRepresentationForProblem(this)); strB.append("\n"); - strB.append(this.m_Population.getStringRepresentation()); + strB.append(this.population.getStringRepresentation()); return strB.toString(); } @@ -327,12 +324,12 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S */ @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.identifier = name; } @Override public String getIdentifier() { - return this.m_Identifier; + return this.identifier; } /** @@ -354,12 +351,12 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S */ @Override public Population getPopulation() { - return this.m_Population; + return this.population; } @Override public void setPopulation(Population pop) { - this.m_Population = pop; + this.population = pop; } public String populationTipText() { @@ -377,11 +374,11 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S * @param selection */ public void setParentSelection(InterfaceSelection selection) { - this.m_ParentSelection = selection; + this.parentSelection = selection; } public InterfaceSelection getParentSelection() { - return this.m_ParentSelection; + return this.parentSelection; } public String parentSelectionTipText() { @@ -403,8 +400,8 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S if (myPlot != null) { myPlot.clearAll(); double[][] range = null; - if ((m_Population != null) && (m_Population.size() > 0)) { - range = ((InterfaceDataTypeDouble) this.m_Population.get(0)).getDoubleRange(); + if ((population != null) && (population.size() > 0)) { + range = ((InterfaceDataTypeDouble) this.population.get(0)).getDoubleRange(); } if (range != null) { myPlot.setCornerPoints(range, 0); @@ -421,8 +418,8 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S myPlot = null; } else { double[][] range; - if ((m_Population != null) && (m_Population.size() > 0)) { - range = ((InterfaceDataTypeDouble) this.m_Population.get(0)).getDoubleRange(); + if ((population != null) && (population.size() > 0)) { + range = ((InterfaceDataTypeDouble) this.population.get(0)).getDoubleRange(); } else { range = new double[2][]; range[0] = new double[2]; @@ -504,6 +501,6 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S public void setPopSize(int popSize) { this.popSize = popSize; - m_Population.setTargetSize(popSize); + population.setTargetSize(popSize); } } diff --git a/src/eva2/optimization/strategies/ParticleSubSwarmOptimization.java b/src/eva2/optimization/strategies/ParticleSubSwarmOptimization.java index 10d987d4..36af6239 100644 --- a/src/eva2/optimization/strategies/ParticleSubSwarmOptimization.java +++ b/src/eva2/optimization/strategies/ParticleSubSwarmOptimization.java @@ -54,8 +54,8 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO */ public ParticleSubSwarmOptimization(ParticleSubSwarmOptimization a) { super(a); - if (a.m_BestIndividual != null) { - m_BestIndividual = (AbstractEAIndividual) a.m_BestIndividual.clone(); + if (a.bestIndividual != null) { + bestIndividual = (AbstractEAIndividual) a.bestIndividual.clone(); } maxAllowedSwarmRadiusNormal = a.maxAllowedSwarmRadiusNormal; @@ -250,7 +250,7 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO /** * @tested junit& - * updates the m_BestIndividual member which represents the gbest individual + * updates the bestIndividual member which represents the gbest individual * (i.e. the best position and fitness encountered by any particle of the swarm) * Beware: if a particle enters the swarm its knowledge about its pbest enters the swarm, * on the other side: if a particle leaves the swarm its knowledge leaves the swarm as well. @@ -259,18 +259,18 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO if (getPopulation().size() == 0) { return; } - // First: set m_BestIndividual to any personal best position in the swarm (-> bestindypbest). - // This is necessary because the current m_BestIndividual my came from + // First: set bestIndividual to any personal best position in the swarm (-> bestindypbest). + // This is necessary because the current bestIndividual my came from // an individual that left the swarm and would never be replaced if it // would dominate all remaining positions in the swarm. AbstractEAIndividual bestindy = getPopulation().getBestEAIndividual(); AbstractEAIndividual bestindypbest = (AbstractEAIndividual) bestindy.getData("PersonalBestKey"); - m_BestIndividual = (AbstractEAIndividual) bestindypbest.clone(); + bestIndividual = (AbstractEAIndividual) bestindypbest.clone(); for (int i = 0; i < getPopulation().size(); ++i) { AbstractEAIndividual currentindy = getPopulation().getEAIndividual(i); AbstractEAIndividual currentindypbest = (AbstractEAIndividual) currentindy.getData("PersonalBestKey"); - if (currentindypbest.isDominating(m_BestIndividual)) { - m_BestIndividual = (AbstractEAIndividual) currentindypbest.clone(); + if (currentindypbest.isDominating(bestIndividual)) { + bestIndividual = (AbstractEAIndividual) currentindypbest.clone(); // ++gbestImprovmentsInARow; } //else gbestImprovmentsInARow = 0; } @@ -347,7 +347,7 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO */ public void updateMaxPosDist() { // compute the maximal possible distance in the search space: - AbstractOptimizationProblem prob = (AbstractOptimizationProblem) m_Problem; + AbstractOptimizationProblem prob = (AbstractOptimizationProblem) optimizationProblem; // problem must have called initializeProblem, so that the template is set correctly. This shouls always be the case here... AbstractEAIndividual template = prob.getIndividualTemplate(); if (template == null) { @@ -481,7 +481,7 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO Population tmp = new Population(); tmp.setTargetSize(particleIndices.length); ////////////// - AbstractOptimizationProblem prob = (AbstractOptimizationProblem) m_Problem; + AbstractOptimizationProblem prob = (AbstractOptimizationProblem) optimizationProblem; AbstractEAIndividual template = prob.getIndividualTemplate(); // problem must be inited at this point AbstractEAIndividual tmpIndy; @@ -494,7 +494,7 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO /////////// ParticleSubSwarmOptimization tmpopt = new ParticleSubSwarmOptimization(); - tmpopt.setProblem(this.m_Problem); + tmpopt.setProblem(this.optimizationProblem); tmpopt.evaluatePopulation(tmp); tmpopt.initByPopulation(tmp, false); // + size FCs @@ -605,7 +605,7 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO */ @Override public void setProblem(InterfaceOptimizationProblem problem) { - this.m_Problem = problem; + this.optimizationProblem = problem; updateMaxPosDist(); } @@ -623,11 +623,11 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO for (int i = 0; i < population.size(); ++i) { AbstractEAIndividual indy = population.getEAIndividual(i); - //double dist = metric.distance(m_BestIndividual, indy); - double sqrdDist = EuclideanMetric.squaredEuclideanDistance(AbstractEAIndividual.getDoublePositionShallow(m_BestIndividual), + //double dist = metric.distance(bestIndividual, indy); + double sqrdDist = EuclideanMetric.squaredEuclideanDistance(AbstractEAIndividual.getDoublePositionShallow(bestIndividual), AbstractEAIndividual.getDoublePositionShallow(indy)); - //dist = distance(m_BestIndividual, indy); + //dist = distance(bestIndividual, indy); if (sqrdDist > max) { max = sqrdDist; } @@ -773,7 +773,7 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO } public AbstractEAIndividual getGBestIndividual() { - return m_BestIndividual; + return bestIndividual; } /** diff --git a/src/eva2/optimization/strategies/ParticleSwarmOptimization.java b/src/eva2/optimization/strategies/ParticleSwarmOptimization.java index c87c0084..21e89ff3 100644 --- a/src/eva2/optimization/strategies/ParticleSwarmOptimization.java +++ b/src/eva2/optimization/strategies/ParticleSwarmOptimization.java @@ -48,8 +48,8 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se private static final long serialVersionUID = -149996122795669589L; protected Population population = new Population(); Object[] sortedPop = null; - protected AbstractEAIndividual m_BestIndividual = null; - protected InterfaceOptimizationProblem m_Problem = new F1Problem(); + protected AbstractEAIndividual bestIndividual = null; + protected InterfaceOptimizationProblem optimizationProblem = new F1Problem(); protected boolean checkRange = true; protected boolean checkSpeedLimit = false; protected boolean useAlternative = false; @@ -93,7 +93,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se transient final static String indexKey = "particleIndex"; transient final static String sortedIndexKey = "sortedParticleIndex"; transient final static String dmsGroupIndexKey = "dmsGroupIndex"; - protected String m_Identifier = ""; + protected String indentifier = ""; transient private Vector changeListener; transient private TopoPlot topoPlot = null; /// sleep time so that visual plot can be followed easier @@ -104,15 +104,13 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se private int emaPeriods = 0; // for debugging only transient private static boolean TRACE = false; - transient protected boolean m_Show = false; - transient protected Plot m_Plot; + transient protected boolean show = false; + transient protected Plot plot; private boolean externalInitialPop = false; private static String lastSuccessKey = "successfulUpdate"; // private double lsCandidateRatio=0.25; public ParticleSwarmOptimization() { -// this.m_Topology = new SelectedTag( "Linear", "Grid", "Star", "Multi-Swarm", "Tree", "HPSO", "Random" ); -// m_Topology.setSelectedTag(1); topology = PSOTopologyEnum.grid; algType = new SelectedTag("Inertness", "Constriction"); algType.setSelectedTag(1); @@ -123,14 +121,12 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se public ParticleSwarmOptimization(ParticleSwarmOptimization a) { topology = a.topology; -// if (a.m_Topology != null) -// this.m_Topology = (SelectedTag)a.m_Topology.clone(); if (a.algType != null) { this.algType = (SelectedTag) a.algType.clone(); } this.population = (Population) a.population.clone(); - this.m_Problem = a.m_Problem; - this.m_Identifier = a.m_Identifier; + this.optimizationProblem = a.optimizationProblem; + this.indentifier = a.indentifier; this.initialVelocity = a.initialVelocity; this.speedLimit = a.speedLimit; this.phi1 = a.phi1; @@ -195,9 +191,9 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se @Override public void init() { - if (m_Plot != null) { -// m_Plot.dispose(); - m_Plot = null; + if (plot != null) { +// plot.dispose(); + plot = null; } if (topoPlot != null) { // topoPlot.dispose(); @@ -205,13 +201,13 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se } tracedVelocity = null; if (!externalInitialPop) { - this.m_Problem.initializePopulation(this.population); + this.optimizationProblem.initializePopulation(this.population); } // evaluation needs to be done here now, as its omitted if reset is false initDefaults(this.population); this.evaluatePopulation(this.population); - if (m_BestIndividual == null) { - m_BestIndividual = population.getBestEAIndividual(); + if (bestIndividual == null) { + bestIndividual = population.getBestEAIndividual(); } initByPopulation(null, false); externalInitialPop = false; @@ -282,7 +278,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se addMovingAverage(tracedVelocity, curAvVelAndSpeed, 2. / (emaPeriods + 1)); } } - if (m_Show) { + if (show) { System.out.println(population.getGeneration() + " - abs avg " + curAvVelAndSpeed[curAvVelAndSpeed.length - 1] + ", vect " + Mathematics.getRelativeLength(curAvVelAndSpeed, range) + ", rel " + (curAvVelAndSpeed[curAvVelAndSpeed.length - 1] / Mathematics.getRelativeLength(curAvVelAndSpeed, range))); } } @@ -472,7 +468,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se } } - this.m_BestIndividual = (AbstractEAIndividual) this.population.getBestEAIndividual().clone(); + this.bestIndividual = (AbstractEAIndividual) this.population.getBestEAIndividual().clone(); if (reset) { this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); @@ -538,7 +534,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se * @param population The population that is to be evaluated */ protected void evaluatePopulation(Population population) { - this.m_Problem.evaluate(population); + this.optimizationProblem.evaluate(population); population.incrGeneration(); if (emaPeriods > 0) { traceEMA(population); @@ -678,37 +674,37 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se } protected void plotIndy(double[] curPosition, double[] curVelocity, int index) { - if (this.m_Show) { + if (this.show) { if (curVelocity == null) { - this.m_Plot.setUnconnectedPoint(curPosition[0], curPosition[1], index); + this.plot.setUnconnectedPoint(curPosition[0], curPosition[1], index); } else { - this.m_Plot.setConnectedPoint(curPosition[0], curPosition[1], index); - this.m_Plot.setConnectedPoint(curPosition[0] + curVelocity[0], curPosition[1] + curVelocity[1], index); + this.plot.setConnectedPoint(curPosition[0], curPosition[1], index); + this.plot.setConnectedPoint(curPosition[0] + curVelocity[0], curPosition[1] + curVelocity[1], index); } -// m_Plot = null; +// plot = null; // show(); // if (pop.getGeneration()%15 == 0) { -// this.m_Plot.clearAll(); -// m_Plot.setUnconnectedPoint(-10, -10, 0); -// m_Plot.setUnconnectedPoint(10, 10, 0); +// this.plot.clearAll(); +// plot.setUnconnectedPoint(-10, -10, 0); +// plot.setUnconnectedPoint(10, 10, 0); // } // if (index != 0) return; -// double[] bestPosition = (double[])m_BestIndividual.getData(partBestPosKey); +// double[] bestPosition = (double[])bestIndividual.getData(partBestPosKey); // double[] localBestPos = findNeighbourhoodOptimum(index, population); -// this.m_Plot.setConnectedPoint(curPosition[0], curPosition[1], index+1); -// this.m_Plot.setConnectedPoint(curPosition[0] + curVelocity[0], curPosition[1] + curVelocity[1], index+1); -// this.m_Plot.setConnectedPoint(curPosition[0], curPosition[1], index+2); -// this.m_Plot.setConnectedPoint(bestPosition[0], bestPosition[1], index+2); -// this.m_Plot.setConnectedPoint(curPosition[0], curPosition[1], index+3); -// this.m_Plot.setConnectedPoint(localBestPos[0], localBestPos[1], index+3); +// this.plot.setConnectedPoint(curPosition[0], curPosition[1], index+1); +// this.plot.setConnectedPoint(curPosition[0] + curVelocity[0], curPosition[1] + curVelocity[1], index+1); +// this.plot.setConnectedPoint(curPosition[0], curPosition[1], index+2); +// this.plot.setConnectedPoint(bestPosition[0], bestPosition[1], index+2); +// this.plot.setConnectedPoint(curPosition[0], curPosition[1], index+3); +// this.plot.setConnectedPoint(localBestPos[0], localBestPos[1], index+3); - // this.m_Plot.setConnectedPoint(curPosition[0], curPosition[1], index+1); - // this.m_Plot.setConnectedPoint(localBestPosition[0], localBestPosition[1], index+1); - // this.m_Plot.setConnectedPoint(curPosition[0], curPosition[1], index+1); - // this.m_Plot.setConnectedPoint(bestPosition[0], bestPosition[1], index+1); - // this.m_Plot.setUnconnectedPoint(curPosition[0], curPosition[1], 100*index+1); + // this.plot.setConnectedPoint(curPosition[0], curPosition[1], index+1); + // this.plot.setConnectedPoint(localBestPosition[0], localBestPosition[1], index+1); + // this.plot.setConnectedPoint(curPosition[0], curPosition[1], index+1); + // this.plot.setConnectedPoint(bestPosition[0], bestPosition[1], index+1); + // this.plot.setUnconnectedPoint(curPosition[0], curPosition[1], 100*index+1); } } @@ -1037,7 +1033,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se localBestFitness = ((double[]) (indy).getData(partBestFitKey)).clone(); localBestPosition = ((double[]) (indy).getData(partBestPosKey)).clone(); if (useHistoric) { - bestIndy = m_BestIndividual; + bestIndy = bestIndividual; } else { bestIndy = pop.getBestEAIndividual(); } @@ -1403,11 +1399,11 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se } protected void maybeClearPlot() { - if (((population.getGeneration() % 23) == 0) && isShow() && (m_Plot != null)) { - m_Plot.clearAll(); + if (((population.getGeneration() % 23) == 0) && isShow() && (plot != null)) { + plot.clearAll(); InterfaceDataTypeDouble indy = (InterfaceDataTypeDouble) this.population.get(0); double[][] range = indy.getDoubleRange(); - m_Plot.setCornerPoints(range, 0); + plot.setCornerPoints(range, 0); } } @@ -1424,7 +1420,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se System.out.println(BeanInspector.toString(indy.getData(partVelKey))); } } - if (this.m_Show) { + if (this.show) { this.show(); } sortedPop = null;// to make sure that the last sorted version is not reused @@ -1434,11 +1430,11 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se * Log the best individual so far. */ protected void logBestIndividual() { - if (this.population.getBestEAIndividual().isDominatingDebConstraints(this.m_BestIndividual)) { - this.m_BestIndividual = (AbstractEAIndividual) this.population.getBestEAIndividual().clone(); - this.m_BestIndividual.putData(partBestFitKey, this.m_BestIndividual.getFitness().clone()); - this.m_BestIndividual.putData(partBestPosKey, ((InterfaceDataTypeDouble) this.m_BestIndividual).getDoubleData()); -// System.out.println("new best: "+m_BestIndividual.toString()); + if (this.population.getBestEAIndividual().isDominatingDebConstraints(this.bestIndividual)) { + this.bestIndividual = (AbstractEAIndividual) this.population.getBestEAIndividual().clone(); + this.bestIndividual.putData(partBestFitKey, this.bestIndividual.getFitness().clone()); + this.bestIndividual.putData(partBestPosKey, ((InterfaceDataTypeDouble) this.bestIndividual).getDoubleData()); +// System.out.println("new best: "+bestIndividual.toString()); } } @@ -1453,8 +1449,8 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se this.updateIndividual(i, (AbstractEAIndividual) population.get(i), this.population); } - if (m_Show) { - if (this.m_Problem instanceof Interface2DBorderProblem) { + if (show) { + if (this.optimizationProblem instanceof Interface2DBorderProblem) { DPointSet popRep = new DPointSet(); InterfaceDataTypeDouble tmpIndy1; @@ -1464,7 +1460,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se if (topoPlot == null) { this.topoPlot = new TopoPlot("CBN-Species", "x", "y", a, a); this.topoPlot.setParams(60, 60); - this.topoPlot.setTopology((Interface2DBorderProblem) this.m_Problem); + this.topoPlot.setTopology((Interface2DBorderProblem) this.optimizationProblem); } for (int i = 0; i < this.population.size(); i++) { @@ -1487,8 +1483,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se } protected void updateTopology(Population pop) { -// int topoID = this.m_Topology.getSelectedTag().getID(); -// this.m_Topology = new SelectedTag( "Linear", "Grid", "Star", "Multi-Swarm", "Tree", "HPSO", "Random" ); if (topology == PSOTopologyEnum.dms) { // Dynamic multi-swarm after Liang & Suganthan if (pop.getGeneration() % getDmsRegroupGens() == 0) { dmsLinks = regroupSwarm(pop, getTopologyRange()); @@ -1650,12 +1644,12 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se * This method is simply for debugging. */ protected void show() { - if (this.m_Plot == null) { + if (this.plot == null) { InterfaceDataTypeDouble indy = (InterfaceDataTypeDouble) this.population.get(0); double[][] range = indy.getDoubleRange(); - this.m_Plot = new Plot("PSO " + population.getGeneration(), "x1", "x2", range[0], range[1]); -// this.m_Plot.setUnconnectedPoint(range[0][0], range[1][0], 0); -// this.m_Plot.setUnconnectedPoint(range[0][1], range[1][1], 0); + this.plot = new Plot("PSO " + population.getGeneration(), "x1", "x2", range[0], range[1]); +// this.plot.setUnconnectedPoint(range[0][0], range[1][0], 0); +// this.plot.setUnconnectedPoint(range[0][1], range[1][1], 0); } } @@ -1702,12 +1696,12 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se */ @Override public void setProblem(InterfaceOptimizationProblem problem) { - this.m_Problem = problem; + this.optimizationProblem = problem; } @Override public InterfaceOptimizationProblem getProblem() { - return this.m_Problem; + return this.optimizationProblem; } /** @@ -1721,7 +1715,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se String result = ""; result += "Particle Swarm Optimization:\n"; result += "Optimization Problem: "; - result += this.m_Problem.getStringRepresentationForProblem(this) + "\n"; + result += this.optimizationProblem.getStringRepresentationForProblem(this) + "\n"; result += this.population.getStringRepresentation(); return result; } @@ -1733,12 +1727,12 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se */ @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.indentifier = name; } @Override public String getIdentifier() { - return this.m_Identifier; + return this.indentifier; } /** @@ -1779,7 +1773,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se } } } - m_BestIndividual = pop.getBestEAIndividual(); + bestIndividual = pop.getBestEAIndividual(); } public String populationTipText() { @@ -1792,7 +1786,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se } public AbstractEAIndividual getBestIndividual() { - return m_BestIndividual; + return bestIndividual; } /** @@ -1970,7 +1964,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se } public void setGOEShowProperties(Class cls) { -// this.m_Topology = new SelectedTag( "Linear", "Grid", "Star", "Multi-Swarm", "Tree", "HPSO", "Random" ); // linear, grid, random GenericObjectEditor.setShowProperty(cls, "topologyRange", (topology == PSOTopologyEnum.linear) || (topology == PSOTopologyEnum.grid) || (topology == PSOTopologyEnum.random) || (topology == PSOTopologyEnum.tree) || (topology == PSOTopologyEnum.hpso) || (topology == PSOTopologyEnum.dms)); @@ -2054,16 +2047,16 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se * @return true if swarm visualization is turned on */ public boolean isShow() { - return m_Show; + return show; } /** * @param set swarm visualization (2D) */ public void setShow(boolean show) { - m_Show = show; + this.show = show; if (!show) { - m_Plot = null; + plot = null; } } @@ -2215,9 +2208,9 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se double relDiff; if (personalBestfit[0] != 0) { - relDiff = (personalBestfit[0] - ((InterfaceProblemDouble) m_Problem).evaluate(personalBestPos)[0]) / personalBestfit[0]; + relDiff = (personalBestfit[0] - ((InterfaceProblemDouble) optimizationProblem).evaluate(personalBestPos)[0]) / personalBestfit[0]; } else { - relDiff = (personalBestfit[0] - ((InterfaceProblemDouble) m_Problem).evaluate(personalBestPos)[0]); // absolute diff in this case + relDiff = (personalBestfit[0] - ((InterfaceProblemDouble) optimizationProblem).evaluate(personalBestPos)[0]); // absolute diff in this case }// if (personalBestfit[0]!=((InterfaceProblemDouble)problem).evaluate(personalBestPos)[0]) { if (Math.abs(relDiff) > 1e-20) { System.err.println("Warning: mismatching best fitness by " + relDiff); @@ -2225,7 +2218,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se } if (Math.abs(relDiff) > 1e-10) { System.err.println("partInfo: " + i + " - " + getParticleInfo(population.getEAIndividual(i))); - throw new RuntimeException("Mismatching best fitness!! " + personalBestfit[0] + " vs. " + ((InterfaceProblemDouble) m_Problem).evaluate(personalBestPos)[0]); + throw new RuntimeException("Mismatching best fitness!! " + personalBestfit[0] + " vs. " + ((InterfaceProblemDouble) optimizationProblem).evaluate(personalBestPos)[0]); } ((InterfaceDataTypeDouble) indy).setDoubleGenotype(personalBestPos); indy.setFitness(personalBestfit); diff --git a/src/eva2/optimization/strategies/PopulationBasedIncrementalLearning.java b/src/eva2/optimization/strategies/PopulationBasedIncrementalLearning.java index ec83f752..96ed4485 100644 --- a/src/eva2/optimization/strategies/PopulationBasedIncrementalLearning.java +++ b/src/eva2/optimization/strategies/PopulationBasedIncrementalLearning.java @@ -28,30 +28,30 @@ import eva2.util.annotation.Description; public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, java.io.Serializable { // These variables are necessary for the simple testcase - private InterfaceOptimizationProblem m_Problem = new B1Problem(); - private boolean m_UseElitism = true; - private InterfaceSelection m_SelectionOperator = new SelectBestIndividuals(); - transient private String m_Identifier = ""; - transient private InterfacePopulationChangedEventListener m_Listener; - private Population m_Population = new PBILPopulation(); - private double m_LearningRate = 0.04; - private double m_MutationRate = 0.5; - private double m_MutateSigma = 0.01; - private int m_NumberOfPositiveSamples = 1; - private double[] m_initialProbabilities = ((PBILPopulation) m_Population).getProbabilityVector(); + private InterfaceOptimizationProblem optimizationProblem = new B1Problem(); + private boolean useElitism = true; + private InterfaceSelection selectionOperator = new SelectBestIndividuals(); + transient private String identifier = ""; + transient private InterfacePopulationChangedEventListener populationChangedEventListener; + private Population population = new PBILPopulation(); + private double learningRate = 0.04; + private double mutationRate = 0.5; + private double mutateSigma = 0.01; + private int numberOfPositiveSamples = 1; + private double[] initialProbabilities = ((PBILPopulation) population).getProbabilityVector(); public PopulationBasedIncrementalLearning() { } public PopulationBasedIncrementalLearning(PopulationBasedIncrementalLearning a) { - this.m_Population = (Population) a.m_Population.clone(); - this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone(); - this.m_LearningRate = a.m_LearningRate; - this.m_MutationRate = a.m_MutationRate; - this.m_MutateSigma = a.m_MutateSigma; - this.m_NumberOfPositiveSamples = a.m_NumberOfPositiveSamples; - this.m_UseElitism = a.m_UseElitism; - this.m_SelectionOperator = (InterfaceSelection) a.m_SelectionOperator.clone(); + this.population = (Population) a.population.clone(); + this.optimizationProblem = (InterfaceOptimizationProblem) a.optimizationProblem.clone(); + this.learningRate = a.learningRate; + this.mutationRate = a.mutationRate; + this.mutateSigma = a.mutateSigma; + this.numberOfPositiveSamples = a.numberOfPositiveSamples; + this.useElitism = a.useElitism; + this.selectionOperator = (InterfaceSelection) a.selectionOperator.clone(); } @Override @@ -61,15 +61,15 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j @Override public void init() { - this.m_Problem.initializePopulation(this.m_Population); - if ((m_initialProbabilities != null) && (m_initialProbabilities.length == ((PBILPopulation) m_Population).getProbabilityVector().length)) { - ((PBILPopulation) m_Population).setProbabilityVector(m_initialProbabilities); + this.optimizationProblem.initializePopulation(this.population); + if ((initialProbabilities != null) && (initialProbabilities.length == ((PBILPopulation) population).getProbabilityVector().length)) { + ((PBILPopulation) population).setProbabilityVector(initialProbabilities); } else { - if (m_initialProbabilities != null) { + if (initialProbabilities != null) { System.err.println("Warning: initial probability vector doesnt match in length!"); } } - this.evaluatePopulation(this.m_Population); + this.evaluatePopulation(this.population); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } @@ -84,13 +84,13 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j if (!(pop.getEAIndividual(0) instanceof InterfaceGAIndividual)) { System.err.println("Error: PBIL only works with GAIndividuals!"); } - this.m_Population = new PBILPopulation(); - this.m_Population.addPopulation((Population) pop.clone()); + this.population = new PBILPopulation(); + this.population.addPopulation((Population) pop.clone()); if (reset) { - this.m_Population.init(); - this.evaluatePopulation(this.m_Population); + this.population.init(); + this.evaluatePopulation(this.population); } - ((PBILPopulation) this.m_Population).buildProbabilityVector(); + ((PBILPopulation) this.population).buildProbabilityVector(); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } @@ -100,7 +100,7 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j * @param population The population that is to be evaluated */ private void evaluatePopulation(Population population) { - this.m_Problem.evaluate(population); + this.optimizationProblem.evaluate(population); population.incrGeneration(); } @@ -109,16 +109,14 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j * population of evaluated individuals. */ private Population generateChildren() { - PBILPopulation result = (PBILPopulation) this.m_Population.clone(); + PBILPopulation result = (PBILPopulation) this.population.clone(); Population examples; -// this.m_NormationOperator.computeSelectionProbability(this.population, "Fitness"); - //System.out.println("Population:"+this.population.getSolutionRepresentationFor()); - this.m_SelectionOperator.prepareSelection(this.m_Population); - examples = this.m_SelectionOperator.selectFrom(this.m_Population, this.m_NumberOfPositiveSamples); - //System.out.println("Parents:"+parents.getSolutionRepresentationFor()); - result.learnFrom(examples, this.m_LearningRate); - result.mutateProbabilityVector(this.m_MutationRate, this.m_MutateSigma); + + this.selectionOperator.prepareSelection(this.population); + examples = this.selectionOperator.selectFrom(this.population, this.numberOfPositiveSamples); + result.learnFrom(examples, this.learningRate); + result.mutateProbabilityVector(this.mutationRate, this.mutateSigma); result.initPBIL(); return result; } @@ -130,12 +128,12 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j nextGeneration = this.generateChildren(); this.evaluatePopulation(nextGeneration); - if (this.m_UseElitism) { - elite = this.m_Population.getBestEAIndividual(); - this.m_Population = nextGeneration; - this.m_Population.add(0, elite); + if (this.useElitism) { + elite = this.population.getBestEAIndividual(); + this.population = nextGeneration; + this.population.add(0, elite); } else { - this.m_Population = nextGeneration; + this.population = nextGeneration; } this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } @@ -147,9 +145,9 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j */ @Override public void setProblem(InterfaceOptimizationProblem problem) { - this.m_Problem = problem; - if (m_Problem instanceof AbstractOptimizationProblem) { - if (!(((AbstractOptimizationProblem) m_Problem).getIndividualTemplate() instanceof InterfaceGAIndividual)) { + this.optimizationProblem = problem; + if (optimizationProblem instanceof AbstractOptimizationProblem) { + if (!(((AbstractOptimizationProblem) optimizationProblem).getIndividualTemplate() instanceof InterfaceGAIndividual)) { System.err.println("Error: PBIL only works with GAIndividuals!"); } } @@ -157,19 +155,19 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j @Override public InterfaceOptimizationProblem getProblem() { - return this.m_Problem; + return this.optimizationProblem; } @Override public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { - this.m_Listener = ea; + this.populationChangedEventListener = ea; } @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener == ea) { - m_Listener = null; + if (populationChangedEventListener == ea) { + populationChangedEventListener = null; return true; } else { return false; @@ -180,8 +178,8 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j * Something has changed */ protected void firePropertyChangedEvent(String name) { - if (this.m_Listener != null) { - this.m_Listener.registerPopulationStateChanged(this, name); + if (this.populationChangedEventListener != null) { + this.populationChangedEventListener.registerPopulationStateChanged(this, name); } } @@ -196,8 +194,8 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j String result = ""; result += "Population Based Incremental Learning:\n"; result += "Optimization Problem: "; - result += this.m_Problem.getStringRepresentationForProblem(this) + "\n"; - result += this.m_Population.getStringRepresentation(); + result += this.optimizationProblem.getStringRepresentationForProblem(this) + "\n"; + result += this.population.getStringRepresentation(); return result; } @@ -208,12 +206,12 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j */ @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.identifier = name; } @Override public String getIdentifier() { - return this.m_Identifier; + return this.identifier; } /** @@ -235,12 +233,12 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j */ @Override public Population getPopulation() { - return this.m_Population; + return this.population; } @Override public void setPopulation(Population pop) { - this.m_Population = pop; + this.population = pop; } public String populationTipText() { @@ -251,18 +249,6 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j public InterfaceSolutionSet getAllSolutions() { return new SolutionSet(getPopulation()); } -// /** This method will set the normation method that is to be used. -// * @param normation -// */ -// public void setNormationMethod (InterfaceNormation normation) { -// this.m_NormationOperator = normation; -// } -// public InterfaceNormation getNormationMethod () { -// return this.m_NormationOperator; -// } -// public String normationMethodTipText() { -// return "Select the normation method."; -// } /** * This method will set the selection method that is to be used @@ -270,11 +256,11 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j * @param selection */ public void setSelectionMethod(InterfaceSelection selection) { - this.m_SelectionOperator = selection; + this.selectionOperator = selection; } public InterfaceSelection getSelectionMethod() { - return this.m_SelectionOperator; + return this.selectionOperator; } public String selectionMethodTipText() { @@ -287,11 +273,11 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j * @param elitism */ public void setElitism(boolean elitism) { - this.m_UseElitism = elitism; + this.useElitism = elitism; } public boolean getElitism() { - return this.m_UseElitism; + return this.useElitism; } public String elitismTipText() { @@ -304,14 +290,14 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j * @param LearningRate */ public void setLearningRate(double LearningRate) { - this.m_LearningRate = LearningRate; - if (this.m_LearningRate < 0) { - this.m_LearningRate = 0; + this.learningRate = LearningRate; + if (this.learningRate < 0) { + this.learningRate = 0; } } public double getLearningRate() { - return this.m_LearningRate; + return this.learningRate; } public String learningRateTipText() { @@ -324,17 +310,17 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j * @param m */ public void setMutationRate(double m) { - this.m_MutationRate = m; - if (this.m_MutationRate < 0) { - this.m_MutationRate = 0; + this.mutationRate = m; + if (this.mutationRate < 0) { + this.mutationRate = 0; } - if (this.m_MutationRate > 1) { - this.m_MutationRate = 1; + if (this.mutationRate > 1) { + this.mutationRate = 1; } } public double getMutationRate() { - return this.m_MutationRate; + return this.mutationRate; } public String mutationRateTipText() { @@ -347,14 +333,14 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j * @param m */ public void setMutateSigma(double m) { - this.m_MutateSigma = m; - if (this.m_MutateSigma < 0) { - this.m_MutateSigma = 0; + this.mutateSigma = m; + if (this.mutateSigma < 0) { + this.mutateSigma = 0; } } public double getMutateSigma() { - return this.m_MutateSigma; + return this.mutateSigma; } public String mutateSigmaTipText() { @@ -367,14 +353,14 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j * @param PositiveSamples */ public void setPositiveSamples(int PositiveSamples) { - this.m_NumberOfPositiveSamples = PositiveSamples; - if (this.m_NumberOfPositiveSamples < 1) { - this.m_NumberOfPositiveSamples = 1; + this.numberOfPositiveSamples = PositiveSamples; + if (this.numberOfPositiveSamples < 1) { + this.numberOfPositiveSamples = 1; } } public int getPositiveSamples() { - return this.m_NumberOfPositiveSamples; + return this.numberOfPositiveSamples; } public String positiveSamplesTipText() { @@ -382,10 +368,10 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j } public double[] getInitialProbabilities() { - return m_initialProbabilities; + return initialProbabilities; } public void setInitialProbabilities(double[] probabilities) { - m_initialProbabilities = probabilities; + initialProbabilities = probabilities; } } \ No newline at end of file diff --git a/src/eva2/optimization/strategies/ScatterSearch.java b/src/eva2/optimization/strategies/ScatterSearch.java index aa4c615e..9c97374d 100644 --- a/src/eva2/optimization/strategies/ScatterSearch.java +++ b/src/eva2/optimization/strategies/ScatterSearch.java @@ -44,9 +44,9 @@ import java.util.ArrayList; @Description("A scatter search variant after Rodiguez-Fernandez, J.Egea and J.Banga: Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems, BMC Bioinf. 2006") public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, InterfacePopulationChangedEventListener { - transient private InterfacePopulationChangedEventListener m_Listener = null; - private String m_Identifier = "ScatterSearch"; - private AbstractOptimizationProblem problem = new F1Problem(); + transient private InterfacePopulationChangedEventListener listener = null; + private String identifier = "ScatterSearch"; + private AbstractOptimizationProblem optimizationProblem = new F1Problem(); private Population oldRefSet, refSet = new Population(10); private transient Population combinations = null; private AbstractEAIndividual template = null; @@ -79,7 +79,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, public ScatterSearch(ScatterSearch o) { this.refSet = (Population) o.refSet.clone(); - this.problem = (AbstractOptimizationProblem) o.problem.clone(); + this.optimizationProblem = (AbstractOptimizationProblem) o.optimizationProblem.clone(); this.template = (AbstractEAIndividual) o.template.clone(); this.range = ((InterfaceDataTypeDouble) template).getDoubleRange(); this.refSetSize = o.refSetSize; @@ -104,7 +104,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, @Override public void setProblem(InterfaceOptimizationProblem problem) { - this.problem = (AbstractOptimizationProblem) problem; + this.optimizationProblem = (AbstractOptimizationProblem) problem; } @Override @@ -136,7 +136,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, * @param pop */ private void initRefSet(Population pop) { - problem.evaluate(pop); + optimizationProblem.evaluate(pop); if (TRACE) { System.out.println("building ref set from pop with avg dist " + pop.getPopulationMeasures()[0]); } @@ -156,11 +156,11 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, firstTime = true; refSet = null; combinations = null; - template = problem.getIndividualTemplate(); + template = optimizationProblem.getIndividualTemplate(); if (!(template instanceof InterfaceDataTypeDouble)) { System.err.println("Requiring double data!"); } else { - Object dim = BeanInspector.callIfAvailable(problem, "getProblemDimension", null); + Object dim = BeanInspector.callIfAvailable(optimizationProblem, "getProblemDimension", null); if (dim == null) { System.err.println("Couldnt get problem dimension!"); } @@ -176,15 +176,15 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, * Something has changed */ protected void firePropertyChangedEvent(String name) { - if (this.m_Listener != null) { - this.m_Listener.registerPopulationStateChanged(this, name); + if (this.listener != null) { + this.listener.registerPopulationStateChanged(this, name); } } // public double evaluate(double[] x) { // AbstractEAIndividual indy = (AbstractEAIndividual)template.clone(); // ((InterfaceDataTypeDouble)indy).setDoubleGenotype(x); -// problem.evaluate(indy); +// optimizationProblem.evaluate(indy); // return indy.getFitness(0); // } @Override @@ -224,7 +224,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, } firstTime = false; - problem.evaluatePopulationStart(refSet); + optimizationProblem.evaluatePopulationStart(refSet); int funCallsStart = refSet.getFunctionCalls(); do { if (combinations == null || combinations.size() == 0) { @@ -243,7 +243,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, updateRefSet(refSet, combinations, oldRefSet); } } while (refSet.getFunctionCalls() - funCallsStart < generationCycle); - problem.evaluatePopulationEnd(refSet); + optimizationProblem.evaluatePopulationEnd(refSet); if (TRACE) { System.out.println("Improvements: " + lastImprovementCount); @@ -302,7 +302,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, } AbstractEAIndividual winner = diversifiedPop.getEAIndividual(sel); // evaluate the new indy - problem.evaluate(winner); + optimizationProblem.evaluate(winner); // add it to the newRefSet, increase h newRefSet.add(winner); newRefSet.incrFunctionCalls(); @@ -416,7 +416,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, if (localSearchMethod.getSelectedTagID() == 0) { return localSolverHC(cand, hcSteps); } else { - return PostProcess.localSolverNMS(cand, hcSteps, nelderMeadInitPerturbation, problem); + return PostProcess.localSolverNMS(cand, hcSteps, nelderMeadInitPerturbation, optimizationProblem); } } @@ -425,7 +425,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, // double[] fitBefore = cand.getFitness(); Population hcPop = new Population(1); hcPop.add(cand); - int stepsDone = PostProcess.processWithHC(hcPop, problem, hcSteps); + int stepsDone = PostProcess.processWithHC(hcPop, optimizationProblem, hcSteps); return new Pair(hcPop.getEAIndividual(0), stepsDone); } @@ -548,7 +548,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, Mathematics.projectToRange(combi, range); } ((InterfaceDataTypeDouble) resIndy).setDoubleGenotype(combi); - problem.evaluate(resIndy); + optimizationProblem.evaluate(resIndy); refSet.incrFunctionCalls(); return resIndy; } @@ -735,12 +735,12 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, ///////////// Trivials... @Override public void setIdentifier(String name) { - m_Identifier = name; + identifier = name; } @Override public InterfaceOptimizationProblem getProblem() { - return problem; + return optimizationProblem; } @Override @@ -751,14 +751,14 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, @Override public void addPopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - m_Listener = ea; + listener = ea; } @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener == ea) { - m_Listener = null; + if (listener == ea) { + listener = null; return true; } else { return false; @@ -767,7 +767,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, @Override public String getIdentifier() { - return m_Identifier; + return identifier; } @Override @@ -872,7 +872,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, InterfaceTerminator term, String dataPrefix, AbstractOptimizationProblem problem, InterfacePopulationChangedEventListener listener) { -// problem.initializeProblem(); +// optimizationProblem.initializeProblem(); OptimizationParameters params = specialSS(localSearchSteps, localSearchFitnessFilter, nmInitPerturb, relativeFitCrit, refSetSize, problem, term); diff --git a/src/eva2/optimization/strategies/SimulatedAnnealing.java b/src/eva2/optimization/strategies/SimulatedAnnealing.java index 57397166..ec5e4e74 100644 --- a/src/eva2/optimization/strategies/SimulatedAnnealing.java +++ b/src/eva2/optimization/strategies/SimulatedAnnealing.java @@ -21,29 +21,29 @@ import eva2.util.annotation.Description; public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializable { // These variables are necessary for the simple testcase - private InterfaceOptimizationProblem m_Problem = new B1Problem(); - private int m_MultiRuns = 100; - private int m_FitnessCalls = 100; - private int m_FitnessCallsNeeded = 0; - GAIndividualBinaryData m_Best, m_Test; - public double m_InitialTemperature = 2, m_CurrentTemperature; - public double m_Alpha = 0.9; + private InterfaceOptimizationProblem optimizationProblem = new B1Problem(); + private int multiRuns = 100; + private int fitnessCalls = 100; + private int fitnessCallsNeeded = 0; + GAIndividualBinaryData bestIndividual, testIndividual; + public double initialTemperature = 2, currentTemperature; + public double alpha = 0.9; // These variables are necessary for the more complex LectureGUI enviroment - transient private String m_Identifier = ""; - transient private InterfacePopulationChangedEventListener m_Listener; - private Population m_Population; + transient private String identifier = ""; + transient private InterfacePopulationChangedEventListener populationChangedEventListener; + private Population population; public SimulatedAnnealing() { - this.m_Population = new Population(); - this.m_Population.setTargetSize(10); + this.population = new Population(); + this.population.setTargetSize(10); } public SimulatedAnnealing(SimulatedAnnealing a) { - this.m_Population = (Population) a.m_Population.clone(); - this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone(); - this.m_InitialTemperature = a.m_InitialTemperature; - this.m_CurrentTemperature = a.m_CurrentTemperature; - this.m_Alpha = a.m_Alpha; + this.population = (Population) a.population.clone(); + this.optimizationProblem = (InterfaceOptimizationProblem) a.optimizationProblem.clone(); + this.initialTemperature = a.initialTemperature; + this.currentTemperature = a.currentTemperature; + this.alpha = a.alpha; } @Override @@ -56,9 +56,9 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa */ @Override public void init() { - this.m_Problem.initializePopulation(this.m_Population); - this.m_Problem.evaluate(this.m_Population); - this.m_CurrentTemperature = this.m_InitialTemperature; + this.optimizationProblem.initializePopulation(this.population); + this.optimizationProblem.evaluate(this.population); + this.currentTemperature = this.initialTemperature; this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } @@ -70,11 +70,11 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa */ @Override public void initByPopulation(Population pop, boolean reset) { - this.m_Population = (Population) pop.clone(); - this.m_CurrentTemperature = this.m_InitialTemperature; + this.population = (Population) pop.clone(); + this.currentTemperature = this.initialTemperature; if (reset) { - this.m_Population.init(); - this.m_Problem.evaluate(this.m_Population); + this.population.init(); + this.optimizationProblem.evaluate(this.population); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } } @@ -85,32 +85,32 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa @Override public void optimize() { AbstractEAIndividual indy; - Population original = (Population) this.m_Population.clone(); + Population original = (Population) this.population.clone(); double delta; - for (int i = 0; i < this.m_Population.size(); i++) { - indy = ((AbstractEAIndividual) this.m_Population.get(i)); + for (int i = 0; i < this.population.size(); i++) { + indy = ((AbstractEAIndividual) this.population.get(i)); double tmpD = indy.getMutationProbability(); indy.setMutationProbability(1.0); indy.mutate(); indy.setMutationProbability(tmpD); } - this.m_Problem.evaluate(this.m_Population); - for (int i = 0; i < this.m_Population.size(); i++) { - if (((AbstractEAIndividual) original.get(i)).isDominatingDebConstraints(((AbstractEAIndividual) this.m_Population.get(i)))) { - this.m_Population.remove(i); - this.m_Population.add(i, original.get(i)); + this.optimizationProblem.evaluate(this.population); + for (int i = 0; i < this.population.size(); i++) { + if (((AbstractEAIndividual) original.get(i)).isDominatingDebConstraints(((AbstractEAIndividual) this.population.get(i)))) { + this.population.remove(i); + this.population.add(i, original.get(i)); } else { - delta = this.calculateDelta(((AbstractEAIndividual) original.get(i)), ((AbstractEAIndividual) this.m_Population.get(i))); + delta = this.calculateDelta(((AbstractEAIndividual) original.get(i)), ((AbstractEAIndividual) this.population.get(i))); //System.out.println("delta: " + delta); - if (Math.exp(-delta / this.m_CurrentTemperature) > RNG.randomInt(0, 1)) { - this.m_Population.remove(i); - this.m_Population.add(i, original.get(i)); + if (Math.exp(-delta / this.currentTemperature) > RNG.randomInt(0, 1)) { + this.population.remove(i); + this.population.add(i, original.get(i)); } } } - this.m_CurrentTemperature = this.m_Alpha * this.m_CurrentTemperature; - this.m_Population.incrGeneration(); + this.currentTemperature = this.alpha * this.currentTemperature; + this.population.incrGeneration(); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } @@ -138,36 +138,36 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa */ @Override public void setProblem(InterfaceOptimizationProblem problem) { - this.m_Problem = problem; + this.optimizationProblem = problem; } @Override public InterfaceOptimizationProblem getProblem() { - return this.m_Problem; + return this.optimizationProblem; } /** * This method will init the HillClimber */ public void defaultInit() { - this.m_FitnessCallsNeeded = 0; - this.m_Best = new GAIndividualBinaryData(); - this.m_Best.defaultInit(m_Problem); + this.fitnessCallsNeeded = 0; + this.bestIndividual = new GAIndividualBinaryData(); + this.bestIndividual.defaultInit(optimizationProblem); } /** * This method will optimize */ public void defaultOptimize() { - for (int i = 0; i < m_FitnessCalls; i++) { - this.m_Test = (GAIndividualBinaryData) ((this.m_Best).clone()); - this.m_Test.defaultMutate(); - if (this.m_Test.defaultEvaulateAsMiniBits() < this.m_Best.defaultEvaulateAsMiniBits()) { - this.m_Best = this.m_Test; + for (int i = 0; i < fitnessCalls; i++) { + this.testIndividual = (GAIndividualBinaryData) ((this.bestIndividual).clone()); + this.testIndividual.defaultMutate(); + if (this.testIndividual.defaultEvaulateAsMiniBits() < this.bestIndividual.defaultEvaulateAsMiniBits()) { + this.bestIndividual = this.testIndividual; } - this.m_FitnessCallsNeeded = i; - if (this.m_Best.defaultEvaulateAsMiniBits() == 0) { - i = this.m_FitnessCalls + 1; + this.fitnessCallsNeeded = i; + if (this.bestIndividual.defaultEvaulateAsMiniBits() == 0) { + i = this.fitnessCalls + 1; } } } @@ -180,27 +180,27 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa public static void main(String[] args) { SimulatedAnnealing program = new SimulatedAnnealing(); int TmpMeanCalls = 0, TmpMeanFitness = 0; - for (int i = 0; i < program.m_MultiRuns; i++) { + for (int i = 0; i < program.multiRuns; i++) { program.defaultInit(); program.defaultOptimize(); - TmpMeanCalls += program.m_FitnessCallsNeeded; - TmpMeanFitness += program.m_Best.defaultEvaulateAsMiniBits(); + TmpMeanCalls += program.fitnessCallsNeeded; + TmpMeanFitness += program.bestIndividual.defaultEvaulateAsMiniBits(); } - TmpMeanCalls /= program.m_MultiRuns; - TmpMeanFitness /= program.m_MultiRuns; - System.out.println("(" + program.m_MultiRuns + "/" + program.m_FitnessCalls + ") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls); + TmpMeanCalls /= program.multiRuns; + TmpMeanFitness /= program.multiRuns; + System.out.println("(" + program.multiRuns + "/" + program.fitnessCalls + ") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls); } @Override public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { - this.m_Listener = ea; + this.populationChangedEventListener = ea; } @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener == ea) { - m_Listener = null; + if (populationChangedEventListener == ea) { + populationChangedEventListener = null; return true; } else { return false; @@ -208,8 +208,8 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa } protected void firePropertyChangedEvent(String name) { - if (this.m_Listener != null) { - this.m_Listener.registerPopulationStateChanged(this, name); + if (this.populationChangedEventListener != null) { + this.populationChangedEventListener.registerPopulationStateChanged(this, name); } } @@ -222,14 +222,14 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa @Override public String getStringRepresentation() { String result = ""; - if (this.m_Population.size() > 1) { - result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n"; + if (this.population.size() > 1) { + result += "Multi(" + this.population.size() + ")-Start Hill Climbing:\n"; } else { result += "Simulated Annealing:\n"; } result += "Optimization Problem: "; - result += this.m_Problem.getStringRepresentationForProblem(this) + "\n"; - result += this.m_Population.getStringRepresentation(); + result += this.optimizationProblem.getStringRepresentationForProblem(this) + "\n"; + result += this.population.getStringRepresentation(); return result; } @@ -240,12 +240,12 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa */ @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.identifier = name; } @Override public String getIdentifier() { - return this.m_Identifier; + return this.identifier; } @@ -268,12 +268,12 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa */ @Override public Population getPopulation() { - return this.m_Population; + return this.population; } @Override public void setPopulation(Population pop) { - this.m_Population = pop; + this.population = pop; } public String populationTipText() { @@ -291,11 +291,11 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa * @return The initial temperature. */ public double getInitialTemperature() { - return this.m_InitialTemperature; + return this.initialTemperature; } public void setInitialTemperature(double pop) { - this.m_InitialTemperature = pop; + this.initialTemperature = pop; } public String initialTemperatureTipText() { @@ -308,13 +308,13 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa * @return The cooling rate. */ public double getAlpha() { - return this.m_Alpha; + return this.alpha; } public void setAlpha(double a) { - this.m_Alpha = a; - if (this.m_Alpha > 1) { - this.m_Alpha = 1.0; + this.alpha = a; + if (this.alpha > 1) { + this.alpha = 1.0; } } diff --git a/src/eva2/optimization/strategies/ThresholdAlgorithm.java b/src/eva2/optimization/strategies/ThresholdAlgorithm.java index 745fdd4d..84932650 100644 --- a/src/eva2/optimization/strategies/ThresholdAlgorithm.java +++ b/src/eva2/optimization/strategies/ThresholdAlgorithm.java @@ -18,29 +18,29 @@ import eva2.util.annotation.Description; public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializable { // These variables are necessary for the simple testcase - private InterfaceOptimizationProblem m_Problem = new B1Problem(); - private int m_MultiRuns = 100; - private int m_FitnessCalls = 100; - private int m_FitnessCallsNeeded = 0; - GAIndividualBinaryData m_Best, m_Test; - public double m_InitialT = 2, m_CurrentT; - public double m_Alpha = 0.9; + private InterfaceOptimizationProblem optimizationProblem = new B1Problem(); + private int multiRuns = 100; + private int fitnessCalls = 100; + private int fitnessCallsNeeded = 0; + GAIndividualBinaryData bestIndividual, testIndividual; + public double initialT = 2, currentT; + public double alpha = 0.9; // These variables are necessary for the more complex LectureGUI enviroment - transient private String m_Identifier = ""; - transient private InterfacePopulationChangedEventListener m_Listener; - private Population m_Population; + transient private String indentifier = ""; + transient private InterfacePopulationChangedEventListener populationChangedEventListener; + private Population population; public ThresholdAlgorithm() { - this.m_Population = new Population(); - this.m_Population.setTargetSize(10); + this.population = new Population(); + this.population.setTargetSize(10); } public ThresholdAlgorithm(ThresholdAlgorithm a) { - this.m_Population = (Population) a.m_Population.clone(); - this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone(); - this.m_InitialT = a.m_InitialT; - this.m_CurrentT = a.m_CurrentT; - this.m_Alpha = a.m_Alpha; + this.population = (Population) a.population.clone(); + this.optimizationProblem = (InterfaceOptimizationProblem) a.optimizationProblem.clone(); + this.initialT = a.initialT; + this.currentT = a.currentT; + this.alpha = a.alpha; } @Override @@ -53,9 +53,9 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa */ @Override public void init() { - this.m_Problem.initializePopulation(this.m_Population); - this.m_Problem.evaluate(this.m_Population); - this.m_CurrentT = this.m_InitialT; + this.optimizationProblem.initializePopulation(this.population); + this.optimizationProblem.evaluate(this.population); + this.currentT = this.initialT; this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } @@ -67,11 +67,11 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa */ @Override public void initByPopulation(Population pop, boolean reset) { - this.m_Population = (Population) pop.clone(); - this.m_CurrentT = this.m_InitialT; + this.population = (Population) pop.clone(); + this.currentT = this.initialT; if (reset) { - this.m_Population.init(); - this.m_Problem.evaluate(this.m_Population); + this.population.init(); + this.optimizationProblem.evaluate(this.population); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } } @@ -82,26 +82,26 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa @Override public void optimize() { AbstractEAIndividual indy; - Population original = (Population) this.m_Population.clone(); + Population original = (Population) this.population.clone(); double delta; - for (int i = 0; i < this.m_Population.size(); i++) { - indy = ((AbstractEAIndividual) this.m_Population.get(i)); + for (int i = 0; i < this.population.size(); i++) { + indy = ((AbstractEAIndividual) this.population.get(i)); double tmpD = indy.getMutationProbability(); indy.setMutationProbability(1.0); indy.mutate(); indy.setMutationProbability(tmpD); } - this.m_Problem.evaluate(this.m_Population); - for (int i = 0; i < this.m_Population.size(); i++) { - delta = this.calculateDelta(((AbstractEAIndividual) original.get(i)), ((AbstractEAIndividual) this.m_Population.get(i))); - if (delta < this.m_CurrentT) { - this.m_Population.remove(i); - this.m_Population.add(i, original.get(i)); + this.optimizationProblem.evaluate(this.population); + for (int i = 0; i < this.population.size(); i++) { + delta = this.calculateDelta(((AbstractEAIndividual) original.get(i)), ((AbstractEAIndividual) this.population.get(i))); + if (delta < this.currentT) { + this.population.remove(i); + this.population.add(i, original.get(i)); } } - this.m_CurrentT = this.m_Alpha * this.m_CurrentT; - this.m_Population.incrGeneration(); + this.currentT = this.alpha * this.currentT; + this.population.incrGeneration(); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); } @@ -129,36 +129,36 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa */ @Override public void setProblem(InterfaceOptimizationProblem problem) { - this.m_Problem = problem; + this.optimizationProblem = problem; } @Override public InterfaceOptimizationProblem getProblem() { - return this.m_Problem; + return this.optimizationProblem; } /** * This method will init the HillClimber */ public void defaultInit() { - this.m_FitnessCallsNeeded = 0; - this.m_Best = new GAIndividualBinaryData(); - this.m_Best.defaultInit(m_Problem); + this.fitnessCallsNeeded = 0; + this.bestIndividual = new GAIndividualBinaryData(); + this.bestIndividual.defaultInit(optimizationProblem); } /** * This method will optimize */ public void defaultOptimize() { - for (int i = 0; i < m_FitnessCalls; i++) { - this.m_Test = (GAIndividualBinaryData) ((this.m_Best).clone()); - this.m_Test.defaultMutate(); - if (this.m_Test.defaultEvaulateAsMiniBits() < this.m_Best.defaultEvaulateAsMiniBits()) { - this.m_Best = this.m_Test; + for (int i = 0; i < fitnessCalls; i++) { + this.testIndividual = (GAIndividualBinaryData) ((this.bestIndividual).clone()); + this.testIndividual.defaultMutate(); + if (this.testIndividual.defaultEvaulateAsMiniBits() < this.bestIndividual.defaultEvaulateAsMiniBits()) { + this.bestIndividual = this.testIndividual; } - this.m_FitnessCallsNeeded = i; - if (this.m_Best.defaultEvaulateAsMiniBits() == 0) { - i = this.m_FitnessCalls + 1; + this.fitnessCallsNeeded = i; + if (this.bestIndividual.defaultEvaulateAsMiniBits() == 0) { + i = this.fitnessCalls + 1; } } } @@ -171,27 +171,27 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa public static void main(String[] args) { ThresholdAlgorithm program = new ThresholdAlgorithm(); int TmpMeanCalls = 0, TmpMeanFitness = 0; - for (int i = 0; i < program.m_MultiRuns; i++) { + for (int i = 0; i < program.multiRuns; i++) { program.defaultInit(); program.defaultOptimize(); - TmpMeanCalls += program.m_FitnessCallsNeeded; - TmpMeanFitness += program.m_Best.defaultEvaulateAsMiniBits(); + TmpMeanCalls += program.fitnessCallsNeeded; + TmpMeanFitness += program.bestIndividual.defaultEvaulateAsMiniBits(); } - TmpMeanCalls /= program.m_MultiRuns; - TmpMeanFitness /= program.m_MultiRuns; - System.out.println("(" + program.m_MultiRuns + "/" + program.m_FitnessCalls + ") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls); + TmpMeanCalls /= program.multiRuns; + TmpMeanFitness /= program.multiRuns; + System.out.println("(" + program.multiRuns + "/" + program.fitnessCalls + ") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls); } @Override public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { - this.m_Listener = ea; + this.populationChangedEventListener = ea; } @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener == ea) { - m_Listener = null; + if (populationChangedEventListener == ea) { + populationChangedEventListener = null; return true; } else { return false; @@ -199,8 +199,8 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa } protected void firePropertyChangedEvent(String name) { - if (this.m_Listener != null) { - this.m_Listener.registerPopulationStateChanged(this, name); + if (this.populationChangedEventListener != null) { + this.populationChangedEventListener.registerPopulationStateChanged(this, name); } } @@ -213,14 +213,14 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa @Override public String getStringRepresentation() { String result = ""; - if (this.m_Population.size() > 1) { - result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n"; + if (this.population.size() > 1) { + result += "Multi(" + this.population.size() + ")-Start Hill Climbing:\n"; } else { result += "Threshold Algorithm:\n"; } result += "Optimization Problem: "; - result += this.m_Problem.getStringRepresentationForProblem(this) + "\n"; - result += this.m_Population.getStringRepresentation(); + result += this.optimizationProblem.getStringRepresentationForProblem(this) + "\n"; + result += this.population.getStringRepresentation(); return result; } @@ -231,12 +231,12 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa */ @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.indentifier = name; } @Override public String getIdentifier() { - return this.m_Identifier; + return this.indentifier; } /** @@ -258,12 +258,12 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa */ @Override public Population getPopulation() { - return this.m_Population; + return this.population; } @Override public void setPopulation(Population pop) { - this.m_Population = pop; + this.population = pop; } public String populationTipText() { @@ -281,11 +281,11 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa * @return The initial temperature. */ public double getInitialT() { - return this.m_InitialT; + return this.initialT; } public void setInitialT(double pop) { - this.m_InitialT = pop; + this.initialT = pop; } public String initialTTipText() { @@ -298,13 +298,13 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa * @return The initial temperature. */ public double getAlpha() { - return this.m_Alpha; + return this.alpha; } public void setAlpha(double a) { - this.m_Alpha = a; - if (this.m_Alpha > 1) { - this.m_Alpha = 1.0; + this.alpha = a; + if (this.alpha > 1) { + this.alpha = 1.0; } } diff --git a/src/eva2/optimization/strategies/Tribes.java b/src/eva2/optimization/strategies/Tribes.java index b932e960..839e8756 100644 --- a/src/eva2/optimization/strategies/Tribes.java +++ b/src/eva2/optimization/strategies/Tribes.java @@ -135,9 +135,9 @@ import java.util.List; public class Tribes implements InterfaceOptimizer, java.io.Serializable { public static final boolean TRACE = false; - protected String m_Identifier = "TRIBES"; - transient private InterfacePopulationChangedEventListener m_Listener = null; - protected AbstractOptimizationProblem m_problem; + protected String identifier = "TRIBES"; + transient private InterfacePopulationChangedEventListener listener = null; + protected AbstractOptimizationProblem optimizationProblem; protected Population population; public static int maxExplorerNb = 200; public static int maxMemoryNb = 300; @@ -165,8 +165,8 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable { protected int initExplorerNb = 3; // Number of explorers at the very beginning // use full range (0) or subspace (1) for init options 0 and 1 protected int rangeInitType = 1; - private boolean m_Show = false; - transient protected Plot m_Plot = null; + private boolean show = false; + transient protected Plot plot = null; // private int useAnchors = 0; // use anchors to detect environment changes? @Override @@ -200,7 +200,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable { @Override public void setProblem(InterfaceOptimizationProblem problem) { // System.out.println("TRIBES.SetProblem()"); - m_problem = (AbstractOptimizationProblem) problem; + optimizationProblem = (AbstractOptimizationProblem) problem; range = null; if (problem instanceof InterfaceHasInitRange) { initRange = (double[][]) ((InterfaceHasInitRange) problem).getInitRange(); @@ -215,7 +215,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable { // System.out.println("TRIBES.init()"); // Generate a swarm swarm = new TribesSwarm(this, range, initRange); // TODO initRange is hard coded equal to problem range - //swarm.generateSwarm(initExplorerNb, initType, m_problem); + //swarm.generateSwarm(initExplorerNb, initType, optimizationProblem); // swarm.displaySwarm(swarm,out); // print("\n Best after init: "+swarm.Best.position.fitness,out); @@ -232,7 +232,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable { population.addAll(swarm.toPopulation()); population.init(); // necessary to allow for multi-runs - if (m_Show) { + if (show) { show(); } @@ -274,15 +274,15 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable { } //* initOption Options: 0 - random, 1 - on the bounds, 2 - sunny spell, 3 - around a center //* rangeInitType for options 0,1: 1 means use initRange, 0 use default range - swarm.generateSwarm(initExplorerNb, initOption, rangeInitType, m_problem); + swarm.generateSwarm(initExplorerNb, initOption, rangeInitType, optimizationProblem); } iter++; - m_problem.evaluatePopulationStart(population); + optimizationProblem.evaluatePopulationStart(population); swarm.setSwarmSize(); // swarm.Best.positionPrev = swarm.Best.position; - swarm.moveSwarm(range, new TribesParam(), informOption, m_problem); //*** HERE IT MOVES and EVALUATES + swarm.moveSwarm(range, new TribesParam(), informOption, optimizationProblem); //*** HERE IT MOVES and EVALUATES //public void moveSwarm(double[][] range, int fitnessSize, TribesParam pb, TribesSwarm swarm, // int informOption, AbstractOptimizationProblem prob) { @@ -297,7 +297,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable { adapt = iter; // Memorize at which iteration adaptation occurs for (int i = 0; i < swarm.getTribeCnt(); i++) { - swarm.reinitTribe(i, rangeInitType, m_problem); + swarm.reinitTribe(i, rangeInitType, optimizationProblem); } } } @@ -311,16 +311,16 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable { if (adaptThreshold >= adaptMax) { adapt = iter; // Memorize at which iteration adaptation occurs - swarm.adaptSwarm(rangeInitType, m_problem); // Re´alise l'adaptation + swarm.adaptSwarm(rangeInitType, optimizationProblem); // Re´alise l'adaptation } } } population.clear(); population.addAll(swarm.toPopulation()); - if (m_Show) { + if (show) { plotAll(population); } - m_problem.evaluatePopulationEnd(population); + optimizationProblem.evaluatePopulationEnd(population); // this.population.incrFunctionCallsby(evals); this.population.incrGeneration(); @@ -348,12 +348,12 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable { // TODO private void plotIndy(double[] curPosition, double[] curVelocity, int index) { - if (this.m_Show) { + if (this.show) { if (curVelocity == null) { - this.m_Plot.setUnconnectedPoint(curPosition[0], curPosition[1], index); + this.plot.setUnconnectedPoint(curPosition[0], curPosition[1], index); } else { - this.m_Plot.setConnectedPoint(curPosition[0], curPosition[1], index); - this.m_Plot.setConnectedPoint(curPosition[0] + curVelocity[0], curPosition[1] + curVelocity[1], index); + this.plot.setConnectedPoint(curPosition[0], curPosition[1], index); + this.plot.setConnectedPoint(curPosition[0] + curVelocity[0], curPosition[1] + curVelocity[1], index); } } } @@ -362,14 +362,14 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable { * This method is simply for debugging. */ protected void show() { - if (this.m_Plot == null) { + if (this.plot == null) { // InterfaceDataTypeDouble indy = (InterfaceDataTypeDouble)this.population.get(0); // double[][] range = indy.getDoubleRange(); // double[] tmpD = new double[2]; // tmpD[0] = 0; // tmpD[1] = 0; - this.m_Plot = new Plot("TRIBES " + population.getGeneration(), "x1", "x2", range[0], range[1]); -// this.m_Plot.setCornerPoints(range, 0); + this.plot = new Plot("TRIBES " + population.getGeneration(), "x1", "x2", range[0], range[1]); +// this.plot.setCornerPoints(range, 0); } } @@ -623,14 +623,14 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable { */ @Override public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { - this.m_Listener = ea; + this.listener = ea; } @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener == ea) { - m_Listener = null; + if (listener == ea) { + listener = null; return true; } else { return false; @@ -638,8 +638,8 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable { } protected void firePropertyChangedEvent(String name) { - if (this.m_Listener != null) { - this.m_Listener.registerPopulationStateChanged(this, name); + if (this.listener != null) { + this.listener.registerPopulationStateChanged(this, name); } } @@ -649,12 +649,12 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable { @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.identifier = name; } @Override public String getIdentifier() { - return m_Identifier; + return identifier; } @Override @@ -664,7 +664,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable { @Override public InterfaceOptimizationProblem getProblem() { - return m_problem; + return optimizationProblem; } @Override @@ -730,19 +730,19 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable { // } /** - * @return the m_Show + * @return the show */ public boolean isShow() { - return m_Show; + return show; } /** - * @param show the m_Show to set + * @param show the show to set */ public void setShow(boolean show) { - m_Show = show; + this.show = show; if (!show) { - m_Plot = null; + plot = null; } } } diff --git a/src/eva2/optimization/strategies/WingedMultiObjectiveEA.java b/src/eva2/optimization/strategies/WingedMultiObjectiveEA.java index 607659d7..edcdc56c 100644 --- a/src/eva2/optimization/strategies/WingedMultiObjectiveEA.java +++ b/src/eva2/optimization/strategies/WingedMultiObjectiveEA.java @@ -22,31 +22,31 @@ import eva2.util.annotation.Description; @Description("This is Evolutionary Multi-Criteria Optimization Algorithm hybridized with Local Searchers to span the Pareto-Front.") public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Serializable { - private InterfaceOptimizer m_MOOptimizer = new MultiObjectiveEA(); - private InterfaceOptimizer m_SOOptimizer = new GeneticAlgorithm(); - private InterfaceOptimizer[] m_SOOptimizers; - private Population m_Population = new Population(); - private int m_MigrationRate = 5; - private int m_OutputDimension = 2; - private InterfaceOptimizationProblem m_Problem = new FM0Problem(); - private String m_Identifier = ""; - transient private InterfacePopulationChangedEventListener m_Listener; + private InterfaceOptimizer multiObjectiveEA = new MultiObjectiveEA(); + private InterfaceOptimizer singleObjectiveEA = new GeneticAlgorithm(); + private InterfaceOptimizer[] singleObjectiveOptimizers; + private Population population = new Population(); + private int migrationRate = 5; + private int outputDimension = 2; + private InterfaceOptimizationProblem optimizationProblem = new FM0Problem(); + private String identifier = ""; + transient private InterfacePopulationChangedEventListener populationChangedEventListener; public WingedMultiObjectiveEA() { } public WingedMultiObjectiveEA(WingedMultiObjectiveEA a) { - this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone(); - this.m_MOOptimizer = (InterfaceOptimizer) a.m_MOOptimizer.clone(); - this.m_SOOptimizer = (InterfaceOptimizer) a.m_SOOptimizer.clone(); - if (a.m_SOOptimizers != null) { - this.m_SOOptimizers = new InterfaceOptimizer[a.m_SOOptimizers.length]; - for (int i = 0; i < this.m_SOOptimizers.length; i++) { - this.m_SOOptimizers[i] = (InterfaceOptimizer) a.m_SOOptimizers[i].clone(); + this.optimizationProblem = (InterfaceOptimizationProblem) a.optimizationProblem.clone(); + this.multiObjectiveEA = (InterfaceOptimizer) a.multiObjectiveEA.clone(); + this.singleObjectiveEA = (InterfaceOptimizer) a.singleObjectiveEA.clone(); + if (a.singleObjectiveOptimizers != null) { + this.singleObjectiveOptimizers = new InterfaceOptimizer[a.singleObjectiveOptimizers.length]; + for (int i = 0; i < this.singleObjectiveOptimizers.length; i++) { + this.singleObjectiveOptimizers[i] = (InterfaceOptimizer) a.singleObjectiveOptimizers[i].clone(); } } - this.m_MigrationRate = a.m_MigrationRate; - this.m_Population = (Population) a.m_Population.clone(); + this.migrationRate = a.migrationRate; + this.population = (Population) a.population.clone(); } @Override @@ -56,19 +56,19 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria @Override public void init() { - if (this.m_Problem instanceof AbstractMultiObjectiveOptimizationProblem) { - AbstractMultiObjectiveOptimizationProblem tmpProb = (AbstractMultiObjectiveOptimizationProblem) this.m_Problem; + if (this.optimizationProblem instanceof AbstractMultiObjectiveOptimizationProblem) { + AbstractMultiObjectiveOptimizationProblem tmpProb = (AbstractMultiObjectiveOptimizationProblem) this.optimizationProblem; AbstractMultiObjectiveOptimizationProblem tmpP; MOSOWeightedFitness tmpWF; PropertyDoubleArray tmpDA; - int dim = this.m_OutputDimension; + int dim = this.outputDimension; double[] weights; // dim = tmpProb.getOutputDimension(); - this.m_MOOptimizer.setProblem((InterfaceOptimizationProblem) this.m_Problem.clone()); - this.m_MOOptimizer.init(); - this.m_SOOptimizers = new InterfaceOptimizer[dim]; + this.multiObjectiveEA.setProblem((InterfaceOptimizationProblem) this.optimizationProblem.clone()); + this.multiObjectiveEA.init(); + this.singleObjectiveOptimizers = new InterfaceOptimizer[dim]; for (int i = 0; i < dim; i++) { - tmpP = (AbstractMultiObjectiveOptimizationProblem) this.m_Problem.clone(); + tmpP = (AbstractMultiObjectiveOptimizationProblem) this.optimizationProblem.clone(); weights = new double[dim]; for (int j = 0; j < dim; j++) { weights[j] = 0; @@ -78,13 +78,13 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria tmpWF = new MOSOWeightedFitness(); tmpWF.setWeights(tmpDA); tmpP.setMOSOConverter(tmpWF); - this.m_SOOptimizers[i] = (InterfaceOptimizer) this.m_SOOptimizer.clone(); - this.m_SOOptimizers[i].setProblem(tmpP); - this.m_SOOptimizers[i].init(); + this.singleObjectiveOptimizers[i] = (InterfaceOptimizer) this.singleObjectiveEA.clone(); + this.singleObjectiveOptimizers[i].setProblem(tmpP); + this.singleObjectiveOptimizers[i].init(); } } else { - this.m_SOOptimizer.setProblem(this.m_Problem); - this.m_SOOptimizer.init(); + this.singleObjectiveEA.setProblem(this.optimizationProblem); + this.singleObjectiveEA.init(); } this.communicate(); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); @@ -98,19 +98,19 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria */ @Override public void initByPopulation(Population pop, boolean reset) { - if (this.m_Problem instanceof AbstractMultiObjectiveOptimizationProblem) { - AbstractMultiObjectiveOptimizationProblem tmpProb = (AbstractMultiObjectiveOptimizationProblem) this.m_Problem; + if (this.optimizationProblem instanceof AbstractMultiObjectiveOptimizationProblem) { + AbstractMultiObjectiveOptimizationProblem tmpProb = (AbstractMultiObjectiveOptimizationProblem) this.optimizationProblem; AbstractMultiObjectiveOptimizationProblem tmpP; MOSOWeightedFitness tmpWF; PropertyDoubleArray tmpDA; int dim = 2; double[] weights; // dim = tmpProb.getOutputDimension(); - this.m_MOOptimizer.setProblem((InterfaceOptimizationProblem) this.m_Problem.clone()); - this.m_MOOptimizer.initByPopulation(pop, reset); - this.m_SOOptimizers = new InterfaceOptimizer[dim]; + this.multiObjectiveEA.setProblem((InterfaceOptimizationProblem) this.optimizationProblem.clone()); + this.multiObjectiveEA.initByPopulation(pop, reset); + this.singleObjectiveOptimizers = new InterfaceOptimizer[dim]; for (int i = 0; i < dim; i++) { - tmpP = (AbstractMultiObjectiveOptimizationProblem) this.m_Problem.clone(); + tmpP = (AbstractMultiObjectiveOptimizationProblem) this.optimizationProblem.clone(); weights = new double[dim]; for (int j = 0; j < dim; j++) { weights[j] = 0; @@ -120,13 +120,13 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria tmpWF = new MOSOWeightedFitness(); tmpWF.setWeights(tmpDA); tmpP.setMOSOConverter(tmpWF); - this.m_SOOptimizers[i] = (InterfaceOptimizer) this.m_SOOptimizer.clone(); - this.m_SOOptimizers[i].setProblem(tmpP); - this.m_SOOptimizers[i].initByPopulation(pop, reset); + this.singleObjectiveOptimizers[i] = (InterfaceOptimizer) this.singleObjectiveEA.clone(); + this.singleObjectiveOptimizers[i].setProblem(tmpP); + this.singleObjectiveOptimizers[i].initByPopulation(pop, reset); } } else { - this.m_SOOptimizer.setProblem(this.m_Problem); - this.m_SOOptimizer.initByPopulation(pop, reset); + this.singleObjectiveEA.setProblem(this.optimizationProblem); + this.singleObjectiveEA.initByPopulation(pop, reset); } this.communicate(); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); @@ -138,12 +138,12 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria @Override public void optimize() { - this.m_MOOptimizer.optimize(); - for (int i = 0; i < this.m_SOOptimizers.length; i++) { - this.m_SOOptimizers[i].optimize(); + this.multiObjectiveEA.optimize(); + for (int i = 0; i < this.singleObjectiveOptimizers.length; i++) { + this.singleObjectiveOptimizers[i].optimize(); } - this.m_Population.incrGeneration(); - if ((this.m_Population.getGeneration() % this.m_MigrationRate) == 0) { + this.population.incrGeneration(); + if ((this.population.getGeneration() % this.migrationRate) == 0) { this.communicate(); } @@ -157,21 +157,21 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria */ private void communicate() { int oldFunctionCalls; - this.m_Population.clear(); - this.m_Population.setFunctionCalls(0); + this.population.clear(); + this.population.setFunctionCalls(0); Population pop; // first collect all the data - pop = (Population) this.m_MOOptimizer.getPopulation().clone(); - this.m_Population.addPopulation(pop); - this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls()); - for (int i = 0; i < this.m_SOOptimizers.length; i++) { - pop = (Population) this.m_SOOptimizers[i].getPopulation().clone(); - this.m_Population.addPopulation(pop); - this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls()); + pop = (Population) this.multiObjectiveEA.getPopulation().clone(); + this.population.addPopulation(pop); + this.population.incrFunctionCallsBy(pop.getFunctionCalls()); + for (int i = 0; i < this.singleObjectiveOptimizers.length; i++) { + pop = (Population) this.singleObjectiveOptimizers[i].getPopulation().clone(); + this.population.addPopulation(pop); + this.population.incrFunctionCallsBy(pop.getFunctionCalls()); } - oldFunctionCalls = this.m_Population.getFunctionCalls(); - this.m_Problem.evaluate(this.m_Population); - this.m_Population.setFunctionCalls(oldFunctionCalls); + oldFunctionCalls = this.population.getFunctionCalls(); + this.optimizationProblem.evaluate(this.population); + this.population.setFunctionCalls(oldFunctionCalls); this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED); // double plotValue = (this.problem.getDoublePlotValue(this.population)).doubleValue(); // now they are synchronized lets migrate @@ -182,29 +182,29 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria * This method implements the migration between the optimzers */ private void migrate() { - AbstractEAIndividual[] bestIndys = new AbstractEAIndividual[this.m_OutputDimension]; + AbstractEAIndividual[] bestIndys = new AbstractEAIndividual[this.outputDimension]; double tmpF1, tmpF2; // for each dimension find the best - for (int i = 0; i < this.m_OutputDimension; i++) { - bestIndys[i] = (AbstractEAIndividual) ((AbstractEAIndividual) this.m_Population.get(0)).clone(); + for (int i = 0; i < this.outputDimension; i++) { + bestIndys[i] = (AbstractEAIndividual) ((AbstractEAIndividual) this.population.get(0)).clone(); tmpF1 = bestIndys[i].getFitness(i); // for each individual find the best - for (int j = 0; j < this.m_Population.size(); j++) { - if (((AbstractEAIndividual) this.m_Population.get(j)).getFitness(i) < tmpF1) { - bestIndys[i] = (AbstractEAIndividual) ((AbstractEAIndividual) this.m_Population.get(j)).clone(); + for (int j = 0; j < this.population.size(); j++) { + if (((AbstractEAIndividual) this.population.get(j)).getFitness(i) < tmpF1) { + bestIndys[i] = (AbstractEAIndividual) ((AbstractEAIndividual) this.population.get(j)).clone(); tmpF1 = bestIndys[i].getFitness(i); } } } // now perform the migration AbstractEAIndividual tmpIndy; - for (int i = 0; i < this.m_OutputDimension; i++) { + for (int i = 0; i < this.outputDimension; i++) { tmpIndy = (AbstractEAIndividual) bestIndys[i].clone(); - this.m_MOOptimizer.getProblem().evaluate(tmpIndy); - this.m_MOOptimizer.getPopulation().set(i, tmpIndy); + this.multiObjectiveEA.getProblem().evaluate(tmpIndy); + this.multiObjectiveEA.getPopulation().set(i, tmpIndy); tmpIndy = (AbstractEAIndividual) bestIndys[i].clone(); - this.m_SOOptimizers[i].getProblem().evaluate(tmpIndy); - this.m_SOOptimizers[i].getPopulation().set(0, bestIndys[i]); + this.singleObjectiveOptimizers[i].getProblem().evaluate(tmpIndy); + this.singleObjectiveOptimizers[i].getPopulation().set(0, bestIndys[i]); } } @@ -215,14 +215,14 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria */ @Override public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { - this.m_Listener = ea; + this.populationChangedEventListener = ea; } @Override public boolean removePopulationChangedEventListener( InterfacePopulationChangedEventListener ea) { - if (m_Listener == ea) { - m_Listener = null; + if (populationChangedEventListener == ea) { + populationChangedEventListener = null; return true; } else { return false; @@ -233,8 +233,8 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria * Something has changed */ protected void firePropertyChangedEvent(String name) { - if (this.m_Listener != null) { - this.m_Listener.registerPopulationStateChanged(this, name); + if (this.populationChangedEventListener != null) { + this.populationChangedEventListener.registerPopulationStateChanged(this, name); } } @@ -245,12 +245,12 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria */ @Override public void setProblem(InterfaceOptimizationProblem problem) { - this.m_Problem = problem; + this.optimizationProblem = problem; } @Override public InterfaceOptimizationProblem getProblem() { - return this.m_Problem; + return this.optimizationProblem; } /** @@ -264,8 +264,8 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria String result = ""; result += "EMO:\n"; result += "Optimization Problem: "; - result += this.m_Problem.getStringRepresentationForProblem(this) + "\n"; - result += this.m_Population.getStringRepresentation(); + result += this.optimizationProblem.getStringRepresentationForProblem(this) + "\n"; + result += this.population.getStringRepresentation(); return result; } @@ -276,12 +276,12 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria */ @Override public void setIdentifier(String name) { - this.m_Identifier = name; + this.identifier = name; } @Override public String getIdentifier() { - return this.m_Identifier; + return this.identifier; } /** @@ -303,12 +303,12 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria */ @Override public Population getPopulation() { - return this.m_Population; + return this.population; } @Override public void setPopulation(Population pop) { - this.m_Population = pop; + this.population = pop; } public String populationTipText() { @@ -326,11 +326,11 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria * @return The current optimizing method */ public InterfaceOptimizer getMOOptimizer() { - return this.m_MOOptimizer; + return this.multiObjectiveEA; } public void setMOOptimizer(InterfaceOptimizer b) { - this.m_MOOptimizer = b; + this.multiObjectiveEA = b; } public String mOOptimizerTipText() { @@ -343,11 +343,11 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria * @return The current optimizing method */ public InterfaceOptimizer getSOOptimizer() { - return this.m_SOOptimizer; + return this.singleObjectiveEA; } public void setSOOptimizer(InterfaceOptimizer b) { - this.m_SOOptimizer = b; + this.singleObjectiveEA = b; } public String sOOptimizerTipText() { @@ -360,11 +360,11 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria * @return The current optimizing method */ public int getMigrationRate() { - return this.m_MigrationRate; + return this.migrationRate; } public void setMigrationRate(int b) { - this.m_MigrationRate = b; + this.migrationRate = b; } public String migrationRateTipText() {