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Added support for multiple listeners to DE

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This commit is contained in:
Michael de Paly 2010-02-02 01:30:49 +00:00
parent e504a92519
commit c955f4a955
1 changed files with 69 additions and 6 deletions
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75
src/eva2/server/go/strategies/DifferentialEvolution.java
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@ -48,7 +48,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
private boolean randomizeFKLambda = false;
private boolean generational = true;
private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
transient private Vector<InterfacePopulationChangedEventListener> m_Listener=new Vector<InterfacePopulationChangedEventListener>();
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
@ -179,6 +179,55 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
return result;
}
/**
* This method returns a difference vector between two random individuals from the population.
* This method should make sure that delta is not zero.
*
* @param pop The population to choose from
* @return The delta vector
*/
private double[] fetchDeltaCurrentRandom(Population pop,InterfaceDataTypeDouble indy) {
double[] x1, x2;
double[] result;
boolean isEmpty;
int iterations = 0;
x1 = indy.getDoubleData();
if (parents != null) parents.add((AbstractEAIndividual)indy);
result = new double[x1.length];
isEmpty = true;
AbstractEAIndividual x2Indy = null;
while (isEmpty && (iterations < pop.size())) {
x2Indy = getRandomIndy(pop);
x2 = getGenotype(x2Indy);
for (int i = 0; i < x1.length; i++) {
result[i] = x1[i] - x2[i];
isEmpty = (isEmpty && (result[i]==0));
}
iterations++;
}
if (!isEmpty && (parents != null)) parents.add(x2Indy);
while (isEmpty) {
// for n (popSize) iterations there were only zero vectors found
// so now the hard way: construct a random vector
for (int i = 0; i < x1.length; i++) {
if (RNG.flipCoin(1/(double)x1.length))
result[i] = 0.01*RNG.gaussianDouble(0.1);
else result[i] = 0;
isEmpty = (isEmpty && (result[i]==0));
}
// single parent! dont add another one
}
return result;
}
/**
* This method will return the delta vector to the best individual
@ -268,6 +317,16 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
}
break;
}
case DE_CurrentToRand : {
// this is DE/current-to-rand/1
double[] rndDelta = this.fetchDeltaRandom(pop);
double[] bestDelta = this.fetchDeltaCurrentRandom(pop, esIndy);
if (parents != null) parents.add(pop.getEAIndividual(parentIndex)); // Add wherever oX is used directly
for (int i = 0; i < oX.length; i++) {
vX[i] = oX[i] + this.getCurrentLambda() * bestDelta[i] + this.getCurrentF() * rndDelta[i];
}
break;
}
case DE2_CurrentToBest : {
// this is DE2 or DE/current-to-best/1
double[] rndDelta = this.fetchDeltaRandom(pop);
@ -426,7 +485,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
m_Population.replaceIndividualAt(nextDoomed, indy);
nextDoomed = getNextDoomed(m_Population, nextDoomed+1);
} else {
if (m_Problem instanceof AbstractMultiObjectiveOptimizationProblem) {
if (m_Problem instanceof AbstractMultiObjectiveOptimizationProblem&indy.getFitness().length>1) {
ReplacementCrowding repl = new ReplacementCrowding();
repl.insertIndividual(indy, m_Population, null);
} else {
@ -548,12 +607,12 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
* @param ea
*/
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
this.m_Listener.add(ea);
}
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener.removeElement(ea)) {
return true;
} else return false;
}
@ -561,7 +620,11 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
* @param name
*/
protected void firePropertyChangedEvent (String name) {
if (this.m_Listener != null) this.m_Listener.registerPopulationStateChanged(this, name);
if (this.m_Listener != null){
for(int i=0;i<this.m_Listener.size();i++){
this.m_Listener.get(i).registerPopulationStateChanged(this, name);
}
}
}
/** This method will set the problem that is to be optimized