Fixing several broken observer pattern implementations
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b363e46ad1
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b24dbac6a1
@ -11,7 +11,7 @@ import java.io.Serializable;
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/**
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* Generalized Rastrigin's function.
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*/
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@Description("Generalized Rastrigins's function.")
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@Description("Generalized Rastrigin's function.")
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public class F6Problem extends AbstractProblemDoubleOffset
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implements InterfaceMultimodalProblem, InterfaceFirstOrderDerivableProblem, InterfaceLocalSearchable, Serializable, InterfaceInterestingHistogram {
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private double m_A = 10;
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@ -173,7 +173,7 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization
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private void plotBestIndy() {
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if (m_Plot != null) {
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double[] curPosition = ((InterfaceDataTypeDouble) m_Population.getBestEAIndividual()).getDoubleData();
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double[] curPosition = ((InterfaceDataTypeDouble) population.getBestEAIndividual()).getDoubleData();
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if (lastBestPlot != null) {
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this.m_Plot.setConnectedPoint(lastBestPlot[0], lastBestPlot[1], 0);
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@ -187,7 +187,7 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization
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protected void plotIndy(double[] curPosition, double[] curVelocity, int index) {
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if (this.m_Show) {
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if (plotBestOnly) {
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if (index != ((Integer) (m_Population.getBestEAIndividual().getData(indexKey)))) {
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if (index != ((Integer) (population.getBestEAIndividual().getData(indexKey)))) {
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return;
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} else {
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// if (lastBestPlot != null) this.m_Plot.setConnectedPoint(lastBestPlot[0], lastBestPlot[1], index);
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@ -314,7 +314,7 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization
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*/
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@Override
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protected double getSpeedLimit(int index) {
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if (index >= ((double) (m_Population.size() * highEnergyRatio))) {
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if (index >= ((double) (population.size() * highEnergyRatio))) {
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return speedLimit;
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} else {
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if (highEnergyRaise == 0.) {
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@ -330,11 +330,11 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization
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protected void startOptimize() {
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super.startOptimize();
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if (detectAnchor >= 0) { // set the new detection anchor individual
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detectAnchor = RNG.randomInt(0, m_Population.size() - 1);
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detectAnchor = RNG.randomInt(0, population.size() - 1);
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if (detectFit == null) {
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detectFit = (m_Population.getIndividual(detectAnchor).getFitness()).clone();
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detectFit = (population.getIndividual(detectAnchor).getFitness()).clone();
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} else {
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System.arraycopy(m_Population.getIndividual(detectAnchor).getFitness(), 0, detectFit, 0, detectFit.length);
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System.arraycopy(population.getIndividual(detectAnchor).getFitness(), 0, detectFit, 0, detectFit.length);
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}
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}
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}
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@ -351,8 +351,8 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization
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double quantumCount = 0.;
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// do what the usual function does plus announce quantum particles
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if (quantumRatio > 0.) {
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for (int i = 0; i < this.m_Population.size(); i++) {
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AbstractEAIndividual indy = (AbstractEAIndividual) m_Population.get(i);
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for (int i = 0; i < this.population.size(); i++) {
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AbstractEAIndividual indy = (AbstractEAIndividual) population.get(i);
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if (i >= quantumCount) {
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indy.putData(partTypeKey, quantumType);
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quantumCount += 1. / quantumRatio;
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@ -405,7 +405,7 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization
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plotBestIndy();
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}
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envHasChanged = detectChange(m_Population);
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envHasChanged = detectChange(this.population);
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// if (envHasChanged) {
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// System.out.println("environmental change detected!");
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@ -427,8 +427,8 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization
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@Override
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protected void logBestIndividual() {
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// log the best individual of the population
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if (envHasChanged || (this.m_Population.getBestEAIndividual().isDominatingDebConstraints(this.m_BestIndividual))) {
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this.m_BestIndividual = (AbstractEAIndividual) this.m_Population.getBestEAIndividual().clone();
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if (envHasChanged || (this.population.getBestEAIndividual().isDominatingDebConstraints(this.m_BestIndividual))) {
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this.m_BestIndividual = (AbstractEAIndividual) this.population.getBestEAIndividual().clone();
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this.m_BestIndividual.putData(partBestFitKey, this.m_BestIndividual.getFitness());
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this.m_BestIndividual.putData(partBestPosKey, ((InterfaceDataTypeDouble) this.m_BestIndividual).getDoubleData());
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//System.out.println("-- best ind set to " + ((InterfaceDataTypeDouble)this.m_BestIndividual).getDoubleData()[0] + "/" + ((InterfaceDataTypeDouble)this.m_BestIndividual).getDoubleData()[1]);
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@ -449,7 +449,7 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization
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switch (changeDetectStrategy.getSelectedTag().getID()) {
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case 0:
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if (detectAnchor >= 0) {
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return !(java.util.Arrays.equals(detectFit, m_Population.getIndividual(detectAnchor).getFitness()));
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return !(java.util.Arrays.equals(detectFit, this.population.getIndividual(detectAnchor).getFitness()));
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} else {
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System.err.println("warning, inconsistency in detectChange");
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}
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@ -501,7 +501,7 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization
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strB.append("Dynamic Particle Swarm Optimization:\nOptimization Problem: ");
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strB.append(this.m_Problem.getStringRepresentationForProblem(this));
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strB.append("\n");
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strB.append(this.m_Population.getStringRepresentation());
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strB.append(this.population.getStringRepresentation());
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return strB.toString();
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}
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@ -14,6 +14,8 @@ import eva2.optimization.problems.InterfaceOptimizationProblem;
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import eva2.util.annotation.Description;
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import eva2.util.annotation.Parameter;
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import java.util.Vector;
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/**
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* Evolution strategies by Rechenberg and Schwefel, but please remember that
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* this only gives the generation strategy and not the coding. But this is the
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@ -35,6 +37,7 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
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@Parameter(description = "Determines whether the +-Strategy should be used.", name = "usePlus")
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protected boolean usePlusStrategy = false;
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protected Population population = new Population();
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protected InterfaceOptimizationProblem optimizationProblem = new B1Problem();
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private InterfaceSelection parentSelection = new SelectRandom();
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@ -44,7 +47,7 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
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protected int origPopSize = -1; // especially for CBN
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private boolean forceOrigPopSize = true;// especially for CBN
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transient private String identifier = "";
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transient private InterfacePopulationChangedEventListener changeListener;
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transient private Vector<InterfacePopulationChangedEventListener> changeListener;
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public static final String esMuParam = "EvolutionStrategyMuParameter";
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public static final String esLambdaParam = "EvolutionStrategyLambdaParameter";
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@ -247,13 +250,16 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
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*/
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@Override
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public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
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this.changeListener = ea;
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if (this.changeListener == null) {
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this.changeListener = new Vector<InterfacePopulationChangedEventListener>();
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}
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this.changeListener.add(ea);
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}
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@Override
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public boolean removePopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
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if (changeListener == ea) {
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changeListener = null;
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public boolean removePopulationChangedEventListener(
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InterfacePopulationChangedEventListener ea) {
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if (changeListener != null && changeListener.removeElement(ea)) {
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return true;
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} else {
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return false;
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@ -262,10 +268,14 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
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/**
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* Something has changed
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*
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* @param name
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*/
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protected void firePropertyChangedEvent(String name) {
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if (this.changeListener != null) {
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this.changeListener.registerPopulationStateChanged(this, name);
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for (int i = 0; i < this.changeListener.size(); i++) {
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this.changeListener.get(i).registerPopulationStateChanged(this, name);
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}
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}
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}
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@ -11,6 +11,9 @@ import eva2.optimization.problems.F1Problem;
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import eva2.optimization.problems.InterfaceOptimizationProblem;
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import eva2.util.annotation.Description;
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import java.util.ArrayList;
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import java.util.List;
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/**
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* Evolutionary programming by Fogel. Works fine but is actually a quite greedy
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* local search strategy solely based on mutation. To prevent any confusion, the
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@ -20,21 +23,21 @@ import eva2.util.annotation.Description;
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@Description("This is a basic Evolutionary Programming scheme.")
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public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Serializable {
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private int m_PopulationSize = 0;
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private Population m_Population = new Population();
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private InterfaceOptimizationProblem m_Problem = new F1Problem();
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private InterfaceSelection m_EnvironmentSelection = new SelectEPTournaments();
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private String m_Identifier = "";
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transient private InterfacePopulationChangedEventListener m_Listener;
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private int populationSize = 0;
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private Population population = new Population();
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private InterfaceOptimizationProblem optimizationProblem = new F1Problem();
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private InterfaceSelection environmentSelection = new SelectEPTournaments();
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private String identifier = "";
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transient private List<InterfacePopulationChangedEventListener> populationChangedEventListeners = new ArrayList<>();
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public EvolutionaryProgramming() {
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}
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public EvolutionaryProgramming(EvolutionaryProgramming a) {
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this.m_Population = (Population) a.m_Population.clone();
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this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
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this.m_Identifier = a.m_Identifier;
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this.m_EnvironmentSelection = (InterfaceSelection) a.m_EnvironmentSelection.clone();
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this.population = (Population) a.population.clone();
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this.optimizationProblem = (InterfaceOptimizationProblem) a.optimizationProblem.clone();
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this.identifier = a.identifier;
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this.environmentSelection = (InterfaceSelection)a.environmentSelection.clone();
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}
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@Override
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@ -44,9 +47,9 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
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@Override
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public void init() {
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this.m_Problem.initializePopulation(this.m_Population);
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this.evaluatePopulation(this.m_Population);
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this.m_PopulationSize = this.m_Population.size();
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this.optimizationProblem.initializePopulation(this.population);
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this.evaluatePopulation(this.population);
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this.populationSize = this.population.size();
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this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
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}
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@ -57,10 +60,10 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
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*/
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@Override
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public void initByPopulation(Population pop, boolean reset) {
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this.m_Population = (Population) pop.clone();
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this.population = (Population) pop.clone();
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if (reset) {
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this.m_Population.init();
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this.evaluatePopulation(this.m_Population);
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this.population.init();
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this.evaluatePopulation(this.population);
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this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
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}
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}
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@ -71,7 +74,7 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
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* @param population The population that is to be evaluated
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*/
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private void evaluatePopulation(Population population) {
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this.m_Problem.evaluate(population);
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this.optimizationProblem.evaluate(population);
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population.incrGeneration();
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}
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@ -80,12 +83,12 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
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* population of evaluated individuals.
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*/
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private Population generateChildren() {
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Population result = (Population) this.m_Population.cloneWithoutInds();
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Population result = (Population) this.population.cloneWithoutInds();
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AbstractEAIndividual mutant;
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result.clear();
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for (int i = 0; i < this.m_Population.size(); i++) {
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mutant = (AbstractEAIndividual) ((AbstractEAIndividual) this.m_Population.get(i)).clone();
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for (int i = 0; i < this.population.size(); i++) {
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mutant = (AbstractEAIndividual) ((AbstractEAIndividual) this.population.get(i)).clone();
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double tmpD = mutant.getMutationProbability();
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mutant.setMutationProbability(1.0);
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mutant.mutate();
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@ -99,14 +102,14 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
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public void optimize() {
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Population nextGeneration, parents;
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this.m_EnvironmentSelection.prepareSelection(this.m_Population);
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parents = this.m_EnvironmentSelection.selectFrom(this.m_Population, this.m_PopulationSize);
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this.m_Population.clear();
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this.m_Population.addPopulation(parents);
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this.environmentSelection.prepareSelection(this.population);
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parents = this.environmentSelection.selectFrom(this.population, this.populationSize);
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this.population.clear();
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this.population.addPopulation(parents);
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nextGeneration = this.generateChildren();
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this.evaluatePopulation(nextGeneration);
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nextGeneration.addPopulation(this.m_Population);
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this.m_Population = nextGeneration;
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nextGeneration.addPopulation(this.population);
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this.population = nextGeneration;
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this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
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}
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@ -118,26 +121,21 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
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*/
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@Override
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public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
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this.m_Listener = ea;
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this.populationChangedEventListeners.add(ea);
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}
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@Override
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public boolean removePopulationChangedEventListener(
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InterfacePopulationChangedEventListener ea) {
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if (m_Listener == ea) {
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m_Listener = null;
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return true;
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} else {
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return false;
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}
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return this.populationChangedEventListeners.remove(ea);
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}
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/**
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* Something has changed
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*/
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protected void firePropertyChangedEvent(String name) {
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if (this.m_Listener != null) {
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this.m_Listener.registerPopulationStateChanged(this, name);
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for(InterfacePopulationChangedEventListener l : this.populationChangedEventListeners) {
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l.registerPopulationStateChanged(this, name);
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}
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}
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@ -148,12 +146,12 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
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*/
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@Override
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public void setProblem(InterfaceOptimizationProblem problem) {
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this.m_Problem = problem;
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this.optimizationProblem = problem;
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}
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@Override
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public InterfaceOptimizationProblem getProblem() {
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return this.m_Problem;
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return this.optimizationProblem;
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}
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/**
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@ -167,8 +165,8 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
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String result = "";
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result += "Evolutionary Programming:\n";
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result += "Optimization Problem: ";
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result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
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result += this.m_Population.getStringRepresentation();
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result += this.optimizationProblem.getStringRepresentationForProblem(this) + "\n";
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result += this.population.getStringRepresentation();
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return result;
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}
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@ -179,12 +177,12 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
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*/
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@Override
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public void setIdentifier(String name) {
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this.m_Identifier = name;
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this.identifier = name;
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}
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@Override
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public String getIdentifier() {
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return this.m_Identifier;
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return this.identifier;
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}
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/**
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@ -206,12 +204,12 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
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*/
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@Override
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public Population getPopulation() {
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return this.m_Population;
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return this.population;
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}
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@Override
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public void setPopulation(Population pop) {
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this.m_Population = pop;
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this.population = pop;
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}
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public String populationTipText() {
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@ -229,11 +227,11 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
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* @param selection
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*/
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public void setEnvironmentSelection(InterfaceSelection selection) {
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this.m_EnvironmentSelection = selection;
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this.environmentSelection = selection;
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}
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public InterfaceSelection getEnvironmentSelection() {
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return this.m_EnvironmentSelection;
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return this.environmentSelection;
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}
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public String environmentSelectionTipText() {
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@ -13,6 +13,8 @@ import eva2.optimization.problems.F1Problem;
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import eva2.optimization.problems.InterfaceOptimizationProblem;
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import eva2.util.annotation.Description;
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import java.util.Vector;
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/**
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* The traditional genetic algorithms as devised by Holland. To only special
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* here it the plague factor which reduces the population size to tune from a
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@ -30,7 +32,7 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
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private int plague = 0;
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private int numberOfPartners = 1;
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private String identifier = "";
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transient private InterfacePopulationChangedEventListener popChangedListener;
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transient private Vector<InterfacePopulationChangedEventListener> changeListener;
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public GeneticAlgorithm() {
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}
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@ -163,14 +165,16 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
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*/
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@Override
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public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
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this.popChangedListener = ea;
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if (this.changeListener == null) {
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this.changeListener = new Vector<InterfacePopulationChangedEventListener>();
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}
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this.changeListener.add(ea);
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}
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@Override
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public boolean removePopulationChangedEventListener(
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InterfacePopulationChangedEventListener ea) {
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if (popChangedListener == ea) {
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popChangedListener = null;
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if (changeListener != null && changeListener.removeElement(ea)) {
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return true;
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} else {
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return false;
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@ -178,11 +182,15 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
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}
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/**
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* Something has changed.
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* Something has changed
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*
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* @param name
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*/
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protected void firePropertyChangedEvent(String name) {
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if (this.popChangedListener != null) {
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this.popChangedListener.registerPopulationStateChanged(this, name);
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if (this.changeListener != null) {
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for (int i = 0; i < this.changeListener.size(); i++) {
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this.changeListener.get(i).registerPopulationStateChanged(this, name);
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}
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}
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}
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@ -537,7 +537,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac
|
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ParticleSubSwarmOptimization currentsubswarm = getSubSwarms().get(i);
|
||||
if (currentsubswarm.isActive()) {
|
||||
actCnt++;
|
||||
avgSize += currentsubswarm.m_Population.size();
|
||||
avgSize += currentsubswarm.population.size();
|
||||
}
|
||||
}
|
||||
if (actCnt > 0) {
|
||||
|
||||
@ -114,8 +114,8 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO
|
||||
* and sets the current fitness as the first value
|
||||
*/
|
||||
protected void initIndividuals() {
|
||||
for (int i = 0; i < m_Population.size(); ++i) {
|
||||
AbstractEAIndividual indy = (AbstractEAIndividual) (m_Population.getEAIndividual(i));
|
||||
for (int i = 0; i < population.size(); ++i) {
|
||||
AbstractEAIndividual indy = (AbstractEAIndividual) (population.getEAIndividual(i));
|
||||
initSubSwarmDefaultsOf(indy);
|
||||
}
|
||||
}
|
||||
@ -283,8 +283,8 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO
|
||||
*/
|
||||
public void updateFitnessArchives() {
|
||||
//int lim = 3; // maximal number of fitnessvalues remembered from former iterations
|
||||
for (int i = 0; i < m_Population.size(); ++i) {
|
||||
AbstractEAIndividual indy = (AbstractEAIndividual) m_Population.getEAIndividual(i);
|
||||
for (int i = 0; i < population.size(); ++i) {
|
||||
AbstractEAIndividual indy = (AbstractEAIndividual) population.getEAIndividual(i);
|
||||
Vector<Double> fitArchive_old = (Vector<Double>) (indy.getData(NichePSO.fitArchiveKey));
|
||||
double scalarFitness = sum(indy.getFitness()); // if multiobjective, use the sum of all fitnessvalues (dont use the norm because fitnessvalues may be negative)
|
||||
Double fitness = new Double(scalarFitness);
|
||||
@ -310,8 +310,8 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO
|
||||
* to the std deviation over the last 3 fitness values
|
||||
*/
|
||||
public void updateFitnessStdDev() {
|
||||
for (int i = 0; i < m_Population.size(); ++i) {
|
||||
AbstractEAIndividual currentindy = m_Population.getEAIndividual(i);
|
||||
for (int i = 0; i < population.size(); ++i) {
|
||||
AbstractEAIndividual currentindy = population.getEAIndividual(i);
|
||||
Vector<Double> fitnessArchive = (Vector<Double>) (currentindy.getData(NichePSO.fitArchiveKey));
|
||||
// the stddev is computed over 3 values as suggested in
|
||||
// "a niching particle swarm optimizer" by Brits et al.
|
||||
@ -325,8 +325,8 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO
|
||||
* update the personal best representation if the current individual is better than the pbest
|
||||
*/
|
||||
public void updatePersonalBest() {
|
||||
for (int i = 0; i < m_Population.size(); ++i) {
|
||||
AbstractEAIndividual currentindy = m_Population.getEAIndividual(i);
|
||||
for (int i = 0; i < population.size(); ++i) {
|
||||
AbstractEAIndividual currentindy = population.getEAIndividual(i);
|
||||
AbstractEAIndividual pbest = (AbstractEAIndividual) currentindy.getData("PersonalBestKey");
|
||||
if (currentindy.isDominating(pbest)) {
|
||||
initPersonalBestOf(currentindy);
|
||||
@ -529,10 +529,10 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO
|
||||
* adds a population and its function calls to this.population
|
||||
*/
|
||||
public void addPopulation(Population pop) {
|
||||
m_Population.addPopulation(pop);
|
||||
population.addPopulation(pop);
|
||||
|
||||
// dont peculate the function calls from the added population (which is going to be deleted in NichePSO)
|
||||
m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
|
||||
population.incrFunctionCallsBy(pop.getFunctionCalls());
|
||||
}
|
||||
|
||||
/**
|
||||
@ -544,7 +544,7 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO
|
||||
public boolean addIndividual(IndividualInterface ind) {
|
||||
// nothing to do regarding function calls
|
||||
// old calls were counted in old population new calls are now counted in this population
|
||||
return m_Population.addIndividual(ind);
|
||||
return population.addIndividual(ind);
|
||||
}
|
||||
|
||||
/**
|
||||
@ -571,7 +571,7 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO
|
||||
* removes an individual
|
||||
*/
|
||||
public boolean removeSubIndividual(IndividualInterface ind) {
|
||||
return m_Population.removeMember(ind);
|
||||
return population.removeMember(ind);
|
||||
}
|
||||
|
||||
public void removeSubPopulation(Population pop, boolean allowMissing) { // this is very slow...
|
||||
@ -621,8 +621,8 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO
|
||||
double max = Double.NEGATIVE_INFINITY;
|
||||
//PhenotypeMetric metric = new PhenotypeMetric();
|
||||
|
||||
for (int i = 0; i < m_Population.size(); ++i) {
|
||||
AbstractEAIndividual indy = m_Population.getEAIndividual(i);
|
||||
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),
|
||||
AbstractEAIndividual.getDoublePositionShallow(indy));
|
||||
|
||||
@ -46,7 +46,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
|
||||
* Generated serial version uid.
|
||||
*/
|
||||
private static final long serialVersionUID = -149996122795669589L;
|
||||
protected Population m_Population = new Population();
|
||||
protected Population population = new Population();
|
||||
Object[] sortedPop = null;
|
||||
protected AbstractEAIndividual m_BestIndividual = null;
|
||||
protected InterfaceOptimizationProblem m_Problem = new F1Problem();
|
||||
@ -94,7 +94,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
|
||||
transient final static String sortedIndexKey = "sortedParticleIndex";
|
||||
transient final static String dmsGroupIndexKey = "dmsGroupIndex";
|
||||
protected String m_Identifier = "";
|
||||
transient private InterfacePopulationChangedEventListener m_Listener;
|
||||
transient private Vector<InterfacePopulationChangedEventListener> changeListener;
|
||||
transient private TopoPlot topoPlot = null;
|
||||
/// sleep time so that visual plot can be followed easier
|
||||
protected int sleepTime = 0;
|
||||
@ -128,7 +128,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
|
||||
if (a.algType != null) {
|
||||
this.algType = (SelectedTag) a.algType.clone();
|
||||
}
|
||||
this.m_Population = (Population) a.m_Population.clone();
|
||||
this.population = (Population) a.population.clone();
|
||||
this.m_Problem = a.m_Problem;
|
||||
this.m_Identifier = a.m_Identifier;
|
||||
this.initialVelocity = a.initialVelocity;
|
||||
@ -153,7 +153,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
|
||||
public ParticleSwarmOptimization(int popSize, double p1, double p2, PSOTopologyEnum topo, int topoRange) {
|
||||
this();
|
||||
algType.setSelectedTag(1); // set to constriction
|
||||
m_Population = new Population(popSize);
|
||||
population = new Population(popSize);
|
||||
setPhiValues(p1, p2);
|
||||
topologyRange = topoRange;
|
||||
topology = topo;
|
||||
@ -205,13 +205,13 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
|
||||
}
|
||||
tracedVelocity = null;
|
||||
if (!externalInitialPop) {
|
||||
this.m_Problem.initializePopulation(this.m_Population);
|
||||
this.m_Problem.initializePopulation(this.population);
|
||||
}
|
||||
// evaluation needs to be done here now, as its omitted if reset is false
|
||||
initDefaults(this.m_Population);
|
||||
this.evaluatePopulation(this.m_Population);
|
||||
initDefaults(this.population);
|
||||
this.evaluatePopulation(this.population);
|
||||
if (m_BestIndividual == null) {
|
||||
m_BestIndividual = m_Population.getBestEAIndividual();
|
||||
m_BestIndividual = population.getBestEAIndividual();
|
||||
}
|
||||
initByPopulation(null, false);
|
||||
externalInitialPop = false;
|
||||
@ -409,7 +409,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
|
||||
}
|
||||
|
||||
protected boolean isParticleTypeByIndex(int index, int type) {
|
||||
return isParticleType((AbstractEAIndividual) m_Population.get(index), type);
|
||||
return isParticleType((AbstractEAIndividual) population.get(index), type);
|
||||
}
|
||||
|
||||
protected boolean isParticleType(AbstractEAIndividual indy, int type) {
|
||||
@ -448,31 +448,31 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
|
||||
@Override
|
||||
public void initByPopulation(Population pop, boolean reset) {
|
||||
if (pop != null) {
|
||||
this.m_Population = (Population) pop.clone();
|
||||
this.population = (Population) pop.clone();
|
||||
externalInitialPop = true;
|
||||
}
|
||||
if (reset) {
|
||||
this.m_Population.init();
|
||||
this.population.init();
|
||||
}
|
||||
|
||||
AbstractEAIndividual indy;
|
||||
|
||||
if (!defaultsDone(m_Population.getEAIndividual(0))) {
|
||||
initDefaults(m_Population);
|
||||
if (!defaultsDone(population.getEAIndividual(0))) {
|
||||
initDefaults(population);
|
||||
}
|
||||
|
||||
if (reset) {
|
||||
this.evaluatePopulation(this.m_Population);
|
||||
this.evaluatePopulation(this.population);
|
||||
}
|
||||
|
||||
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 instanceof InterfaceDataTypeDouble) {
|
||||
initIndividualMemory(indy);
|
||||
}
|
||||
}
|
||||
|
||||
this.m_BestIndividual = (AbstractEAIndividual) this.m_Population.getBestEAIndividual().clone();
|
||||
this.m_BestIndividual = (AbstractEAIndividual) this.population.getBestEAIndividual().clone();
|
||||
|
||||
if (reset) {
|
||||
this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
|
||||
@ -484,15 +484,15 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
|
||||
if (topologyRange < 2) {
|
||||
System.err.println("Error, tree/hpso requires topology range of at least 2!");
|
||||
} else {
|
||||
while (getMaxNodes(topologyRange, treeLevels) < m_Population.size()) {
|
||||
while (getMaxNodes(topologyRange, treeLevels) < population.size()) {
|
||||
treeLevels++;
|
||||
}
|
||||
treeOrphans = m_Population.size() - getMaxNodes(topologyRange, treeLevels - 1);
|
||||
treeOrphans = population.size() - getMaxNodes(topologyRange, treeLevels - 1);
|
||||
treeLastFullLevelNodeCnt = (int) Math.pow(topologyRange, treeLevels - 1);
|
||||
}
|
||||
}
|
||||
if (getTopology() == PSOTopologyEnum.dms) {
|
||||
dmsLinks = regroupSwarm(m_Population, getTopologyRange());
|
||||
dmsLinks = regroupSwarm(population, getTopologyRange());
|
||||
}
|
||||
}
|
||||
|
||||
@ -871,18 +871,18 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
|
||||
|
||||
private double[] getSuccessfulVel(int index) {
|
||||
if (true) {
|
||||
return (double[]) m_Population.getEAIndividual(index).getData(lastSuccessKey);
|
||||
return (double[]) population.getEAIndividual(index).getData(lastSuccessKey);
|
||||
} else { // random one
|
||||
ArrayList<Integer> successes = new ArrayList<Integer>();
|
||||
for (int i = 0; i < this.m_Population.size(); i++) {
|
||||
double[] succVel = (double[]) m_Population.getEAIndividual(i).getData(lastSuccessKey);
|
||||
for (int i = 0; i < this.population.size(); i++) {
|
||||
double[] succVel = (double[]) population.getEAIndividual(i).getData(lastSuccessKey);
|
||||
if (succVel != null) {
|
||||
successes.add(new Integer(i));
|
||||
}
|
||||
}
|
||||
if (successes.size() > 0) {
|
||||
int i = successes.get(RNG.randomInt(successes.size()));
|
||||
return (double[]) m_Population.getEAIndividual(i).getData(lastSuccessKey);
|
||||
return (double[]) population.getEAIndividual(i).getData(lastSuccessKey);
|
||||
} else {
|
||||
return null;
|
||||
}
|
||||
@ -1360,10 +1360,10 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
|
||||
updatePopulation();
|
||||
|
||||
// evaluate the population
|
||||
this.evaluatePopulation(this.m_Population);
|
||||
this.evaluatePopulation(this.population);
|
||||
|
||||
// update the individual memory
|
||||
updateSwarmMemory(m_Population);
|
||||
updateSwarmMemory(population);
|
||||
|
||||
// log the best individual of the population
|
||||
logBestIndividual();
|
||||
@ -1403,9 +1403,9 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
|
||||
}
|
||||
|
||||
protected void maybeClearPlot() {
|
||||
if (((m_Population.getGeneration() % 23) == 0) && isShow() && (m_Plot != null)) {
|
||||
if (((population.getGeneration() % 23) == 0) && isShow() && (m_Plot != null)) {
|
||||
m_Plot.clearAll();
|
||||
InterfaceDataTypeDouble indy = (InterfaceDataTypeDouble) this.m_Population.get(0);
|
||||
InterfaceDataTypeDouble indy = (InterfaceDataTypeDouble) this.population.get(0);
|
||||
double[][] range = indy.getDoubleRange();
|
||||
m_Plot.setCornerPoints(range, 0);
|
||||
}
|
||||
@ -1416,8 +1416,8 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
|
||||
*/
|
||||
protected void startOptimize() {
|
||||
if (TRACE) {
|
||||
for (int i = 0; i < m_Population.size(); i++) {
|
||||
AbstractEAIndividual indy = m_Population.getEAIndividual(i);
|
||||
for (int i = 0; i < population.size(); i++) {
|
||||
AbstractEAIndividual indy = population.getEAIndividual(i);
|
||||
System.out.println(BeanInspector.toString(indy.getData(partTypeKey)));
|
||||
System.out.println(BeanInspector.toString(indy.getData(partBestPosKey)));
|
||||
System.out.println(BeanInspector.toString(indy.getData(partBestFitKey)));
|
||||
@ -1434,8 +1434,8 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
|
||||
* Log the best individual so far.
|
||||
*/
|
||||
protected void logBestIndividual() {
|
||||
if (this.m_Population.getBestEAIndividual().isDominatingDebConstraints(this.m_BestIndividual)) {
|
||||
this.m_BestIndividual = (AbstractEAIndividual) this.m_Population.getBestEAIndividual().clone();
|
||||
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());
|
||||
@ -1447,10 +1447,10 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
|
||||
*/
|
||||
protected void updatePopulation() {
|
||||
|
||||
updateTopology(this.m_Population);
|
||||
updateTopology(this.population);
|
||||
|
||||
for (int i = 0; i < this.m_Population.size(); i++) {
|
||||
this.updateIndividual(i, (AbstractEAIndividual) m_Population.get(i), this.m_Population);
|
||||
for (int i = 0; i < this.population.size(); i++) {
|
||||
this.updateIndividual(i, (AbstractEAIndividual) population.get(i), this.population);
|
||||
}
|
||||
|
||||
if (m_Show) {
|
||||
@ -1467,8 +1467,8 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
|
||||
this.topoPlot.setTopology((Interface2DBorderProblem) this.m_Problem);
|
||||
}
|
||||
|
||||
for (int i = 0; i < this.m_Population.size(); i++) {
|
||||
tmpIndy1 = (InterfaceDataTypeDouble) this.m_Population.get(i);
|
||||
for (int i = 0; i < this.population.size(); i++) {
|
||||
tmpIndy1 = (InterfaceDataTypeDouble) this.population.get(i);
|
||||
popRep.addDPoint(new DPoint(tmpIndy1.getDoubleData()[0], tmpIndy1.getDoubleData()[1]));
|
||||
}
|
||||
this.topoPlot.getFunctionArea().addDElement(popRep);
|
||||
@ -1651,33 +1651,47 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
|
||||
*/
|
||||
protected void show() {
|
||||
if (this.m_Plot == null) {
|
||||
InterfaceDataTypeDouble indy = (InterfaceDataTypeDouble) this.m_Population.get(0);
|
||||
InterfaceDataTypeDouble indy = (InterfaceDataTypeDouble) this.population.get(0);
|
||||
double[][] range = indy.getDoubleRange();
|
||||
this.m_Plot = new Plot("PSO " + m_Population.getGeneration(), "x1", "x2", range[0], range[1]);
|
||||
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 method allows you to add the LectureGUI as listener to the Optimizer
|
||||
*
|
||||
* @param ea
|
||||
*/
|
||||
@Override
|
||||
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
|
||||
this.m_Listener = ea;
|
||||
if (this.changeListener == null) {
|
||||
this.changeListener = new Vector<InterfacePopulationChangedEventListener>();
|
||||
}
|
||||
this.changeListener.add(ea);
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean removePopulationChangedEventListener(
|
||||
InterfacePopulationChangedEventListener ea) {
|
||||
if (m_Listener == ea) {
|
||||
m_Listener = null;
|
||||
if (changeListener != null && changeListener.removeElement(ea)) {
|
||||
return true;
|
||||
} else {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Something has changed
|
||||
*
|
||||
* @param name
|
||||
*/
|
||||
protected void firePropertyChangedEvent(String name) {
|
||||
if (this.m_Listener != null) {
|
||||
this.m_Listener.registerPopulationStateChanged(this, name);
|
||||
if (this.changeListener != null) {
|
||||
for (int i = 0; i < this.changeListener.size(); i++) {
|
||||
this.changeListener.get(i).registerPopulationStateChanged(this, name);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -1708,7 +1722,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
|
||||
result += "Particle Swarm Optimization:\n";
|
||||
result += "Optimization Problem: ";
|
||||
result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
|
||||
result += this.m_Population.getStringRepresentation();
|
||||
result += this.population.getStringRepresentation();
|
||||
return result;
|
||||
}
|
||||
|
||||
@ -1740,12 +1754,12 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
|
||||
|
||||
@Override
|
||||
public Population getPopulation() {
|
||||
return this.m_Population;
|
||||
return this.population;
|
||||
}
|
||||
|
||||
@Override
|
||||
public void setPopulation(Population pop) {
|
||||
this.m_Population = pop;
|
||||
this.population = pop;
|
||||
if (pop.size() != pop.getTargetSize()) { // new particle count!
|
||||
tracedVelocity = null;
|
||||
initByPopulation(null, false);
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user