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Changes due to NichePSO and ANPSO (M.Aschoff)

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This commit is contained in:
Marcel Kronfeld
2008-08-28 13:10:25 +00:00
parent 640feb82b7
commit 4c77fb6927
3 changed files with 107 additions and 12 deletions
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11
src/eva2/server/go/individuals/AbstractEAIndividual.java
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@@ -61,6 +61,7 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
// protected String[] m_Identifiers = new String[m_ObjectIncrement];
// protected Object[] m_Objects = new Object[m_ObjectIncrement];
protected HashMap<String,Object> m_dataHash = new HashMap<String,Object>();
protected int individualIndex;
public AbstractEAIndividual() {
m_IDcounter++;
@@ -72,6 +73,15 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
return m_ID;
}
public int getIndividualIndex() {
return individualIndex;
}
public void setIndividualIndex(int index) {
this.individualIndex = index;
}
/** This method will enable you to clone a given individual
* @return The clone
*/
@@ -117,6 +127,7 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
m_ConstraintViolation = individual.m_ConstraintViolation;
m_AreaConst4ParallelViolated = individual.m_AreaConst4ParallelViolated;
m_Marked = individual.m_Marked;
individualIndex = individual.individualIndex;
if (individual.parentIDs != null) {
parentIDs = new Long[individual.parentIDs.length];
System.arraycopy(individual.parentIDs, 0, parentIDs, 0, parentIDs.length);

90
src/eva2/server/go/problems/AbstractOptimizationProblem.java
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@@ -1,8 +1,6 @@
package eva2.server.go.problems;
import java.awt.BorderLayout;
import java.util.BitSet;
import javax.swing.JComponent;
import javax.swing.JPanel;
@@ -11,11 +9,12 @@ import javax.swing.JTextArea;
import eva2.server.go.PopulationInterface;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.InterfaceDataTypeBinary;
import eva2.server.go.individuals.InterfaceDataTypeDouble;
import eva2.server.go.individuals.InterfaceDataTypeInteger;
import eva2.server.go.individuals.InterfaceDataTypePermutation;
import eva2.server.go.operators.distancemetric.InterfaceDistanceMetric;
import eva2.server.go.operators.distancemetric.PhenotypeMetric;
import eva2.server.go.operators.moso.MOSONoConvert;
import eva2.server.go.operators.mutation.InterfaceMutation;
import eva2.server.go.operators.mutation.MutateESFixedStepSize;
import eva2.server.go.operators.postprocess.PostProcess;
import eva2.server.go.populations.Population;
import eva2.server.go.strategies.InterfaceOptimizer;
@@ -196,6 +195,85 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
return m_Template;
}
/**
* This method extracts the individuals from a given population that are assumed to correspond to local or global optima.
* Similar inidviduals are clustered together with a density based clustering method
* @param pop
* @param epsilon maximal allowed improvement of an individual before considered premature (given as distance in the search space)
* @param clusterSigma minimum cluster distance
* @return
*/
public Population extractPotentialOptima(Population pop, double epsilon, double clusterSigma) {
Population potOptima = new Population();
for (int i = 0; i < pop.size(); ++i){
AbstractEAIndividual indy = pop.getEAIndividual(i);
if (isPotentialOptimum(indy, epsilon,-1,-1)){
potOptima.addIndividual(indy);
}
}
Population clusteredPop = (Population)PostProcess.clusterBest(potOptima, clusterSigma, 0, PostProcess.KEEP_LONERS, PostProcess.BEST_ONLY).clone();
return clusteredPop;
}
/**
* This method estimates if the given individual is within epsilon of an optimum (local or global).
* The algorithm tries to improve the given individual locally.
* If it is possible to improve the individual such that its changed position is further than epsilon,
* the individual is considered premature.
* If not, the particle is assumed to correspond to a local or global optimum.
*
* @param orig individual that is to be tested
* @param epsilon maximal allowed improvement before considered premature (given as distance in the search space)
* @param mutationStepSize step size used to mutate the individual in one step
* (if < 0 a default value of 0.0001 is used)
* @param numOfFailures number of unsuccessful improvement steps in a row before an individual is considered to be locally unimproveable
* (if < 0 a default value of 100*problem dimensions is used )
* @return estimation if the given individual is within epsilon of an optimum (local or global)
*/
public boolean isPotentialOptimum(AbstractEAIndividual orig, double epsilon, double mutationStepSize, int numOfFailures){
int stepsCounter = 0; // could be used to limit the maximal number of steps overall
// if not provided reasonable values use defaults:
if (mutationStepSize<0) mutationStepSize = 0.0001;
if (numOfFailures<0) numOfFailures = 100*AbstractEAIndividual.getDoublePosition(this.m_Template).length; // scales the effort with the number of problem dimensions
AbstractEAIndividual indy = (AbstractEAIndividual)orig.clone();
this.evaluate(indy); // indy may be evaluated in a normalised way...
InterfaceDistanceMetric metric = new PhenotypeMetric();
double overallDist = 0;
InterfaceMutation mutator = new MutateESFixedStepSize(mutationStepSize);
for (int i = 0; i < numOfFailures; ++i){
// backup
AbstractEAIndividual old = (AbstractEAIndividual)indy.clone();
// mutate
double tmpD = indy.getMutationProbability();
indy.setMutationProbability(1.0);
mutator.mutate(indy);
++stepsCounter;
indy.setMutationProbability(tmpD);
// evaluate
this.evaluate(indy);
if (old.isDominatingDebConstraints(indy)) {// indy could not be improved
indy = (AbstractEAIndividual)old.clone();
} else { // indy could be improved
i = 0; // the given number of unsuccessful improvement steps should occur in a row
overallDist = metric.distance(orig, indy);
//System.out.println(overallDist);
}
if (overallDist > epsilon) {
return false; // dont waste any more evaluations on this candidate
}
}
if (overallDist < epsilon) {
return true;
}
else return false;
}
/**********************************************************************************************************************
* These are for GUI
*/

18
src/eva2/server/go/strategies/ParticleSwarmOptimization.java
View File

@@ -370,6 +370,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
initIndividualDefaults(indy);
}
indy.SetData(indexKey, i);
indy.setIndividualIndex(i);
}
this.evaluatePopulation(this.m_Population);
@@ -1188,6 +1189,15 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
}
}
}
protected void addSortedIndicesTo(Population pop) {
int origIndex;
for (int i=0; i<pop.size(); i++) {
// cross-link the sorted list for faster access
origIndex = (Integer)((AbstractEAIndividual)sortedPop[i]).getData(indexKey);
((AbstractEAIndividual)pop.get(origIndex)).SetData(sortedIndexKey, new Integer(i));
}
}
protected void updateTopology(Population pop) {
int topoID = this.m_Topology.getSelectedTag().getID();
@@ -1196,12 +1206,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
sortedPop = pop.toArray();
if ((topoID == 3) || (treeStruct>=2)) Arrays.sort(sortedPop, new AbstractEAIndividualComparator());
else Arrays.sort(sortedPop, new AbstractEAIndividualComparator(partBestFitKey));
int k, origIndex;
for (int i=0; i<pop.size(); i++) {
// cross-link the sorted list for faster access
origIndex = (Integer)((AbstractEAIndividual)sortedPop[i]).getData(indexKey);
((AbstractEAIndividual)pop.get(origIndex)).SetData(sortedIndexKey, new Integer(i));
}
addSortedIndicesTo(pop);
}
if (topoID == 3) {
// prepare multi swarm topology
@@ -1400,6 +1405,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
initIndividualDefaults(indy);
initIndividualMemory(indy);
indy.SetData(indexKey, i);
indy.setIndividualIndex(i);
if (TRACE) System.err.println("init indy " + i + " " + AbstractEAIndividual.getDefaultDataString(indy));
}
}