/**
* This method allows an optimizer to register a change in the optimizer.
*
* @param source The source of the event.
* @param name Could be used to indicate the nature of the event.
*/
@Override
public void registerPopulationStateChanged(Object source, String name) {
if (name.equals(Population.NEXT_GENERATION_PERFORMED)) {
Population population = ((InterfaceOptimizer) source).getPopulation();
double x = 100 / this.multiRuns;
if (this.optimizationParameters.getTerminator() instanceof EvaluationTerminator) {
double y =
x
/ (double)
((EvaluationTerminator) this.optimizationParameters.getTerminator())
.getFitnessCalls();
currentProgress = (int) (this.currentRun * x + population.getFunctionCalls() * y);
} else {
currentProgress = (int) (this.currentRun * x);
}
updateStatus(currentProgress);
// data to be stored in file
double tmpd = 0;
StringBuilder tmpLine = new StringBuilder("");
tmpLine.append(population.getFunctionCalls());
tmpLine.append("\t");
tmpLine.append(population.getBestEAIndividual().getFitness(0));
tmpLine.append("\t");
for (int i = 0; i < population.size(); i++) {
tmpd += population.get(i).getFitness(0) / (double) population.size();
}
tmpLine.append("\t");
tmpLine.append(tmpd);
tmpLine.append("\t");
tmpLine.append(population.getWorstEAIndividual().getFitness(0));
// tmpLine.append("\t");
// tmpLine.append(this.optimizationParameters.getProblem().getAdditionalDataValue(population));
this.writeToFile(tmpLine.toString());
Double[] tmpData = new Double[2];
tmpData[0] = (double) population.getFunctionCalls();
// instead of adding simply the best fitness value i'll ask the problem what to show
tmpData[1] = this.optimizationParameters.getProblem().getDoublePlotValue(population);
if (this.plot != null) {
if (this.continueFlag) {
this.plot.setConnectedPoint(
tmpData[0] + this.recentFunctionCalls, tmpData[1], 1000 + this.currentRun);
} else {
this.plot.setConnectedPoint(tmpData[0], tmpData[1], 1000 + this.currentRun);
}
}
this.tmpData.add(tmpData);
}
}
/**
* Make sure that the parameter sets of each population are updated (reinitialized) if a
* population is reinitialized.
*
* @see InterfacePopulationChangedEventListener
*/
@Override
public void registerPopulationStateChanged(Object source, String name) {
if (name.equals(Population.POPULATION_INITIALIZED)) {
Population pop = (Population) source;
CMAParamSet params = (CMAParamSet) (pop.getData(MutateESRankMuCMA.cmaParamsKey));
int mu;
if (pop.hasData(EvolutionStrategies.esMuParam)) {
mu = (Integer) pop.getData(EvolutionStrategies.esMuParam);
} else {
System.err.println("Unknown mu in reinit! using lambda/2...");
mu = pop.size() / 2;
}
pop.putData(
MutateESRankMuCMA.cmaParamsKey,
CMAParamSet.initCMAParams(params, mu, pop.size(), pop, params.firstSigma));
}
}
/**
* The subswarm is deactivated and the particles indices are returned. They are to be
* reinitialized into the mainswarm.
*/
@Override
public int[] deactivateSubswarm(
ParticleSubSwarmOptimization subswarm, ParticleSubSwarmOptimization mainswarm) {
if (!subswarm.isActive()) {
System.out.println("deactivateSubSwarm: try to deactivate inactive subswarm");
return null;
}
// use the indizes of the deactivated particles for the reinitialized particles (important for
// ANPSO)
Population pop = subswarm.getPopulation();
int[] particleIndices = new int[pop.size()];
for (int i = 0; i < pop.size(); ++i) {
AbstractEAIndividual indy = pop.getEAIndividual(i);
// Integer index = (Integer)indy.getData("particleIndex");
particleIndices[i] = indy.getIndividualIndex(); // index.intValue();
}
subswarm.SetActive(false);
return particleIndices;
}
public boolean isConverged(Population pop) {
Vector<Double> bests = (Vector<Double>) pop.getEAIndividual(0).getData(NichePSO.fitArchiveKey);
if (bests.size() < haltingWindow) {
return false;
} else {
List<Double> lst = bests.subList(bests.size() - haltingWindow, bests.size());
bests = new Vector<>(haltingWindow);
bests.addAll(lst);
for (int i = 1; i < pop.size(); i++) {
for (int k = 0; k < haltingWindow; k++) {
Vector<Double> fitArch =
(Vector<Double>) pop.getEAIndividual(i).getData(NichePSO.fitArchiveKey);
int archIndex =
fitArch.size()
- haltingWindow
+ k; // index within the fitness archive of the current particle, which may be
// larger than the bests list - the tail is important
if (archIndex >= 0 && (fitArch.get(archIndex) < bests.get(k))) {
bests.set(k, fitArch.get(archIndex));
}
}
}
}
// bests contains now the sequence of best fitness values across the last generations
Double historicHWAgo = bests.get(0);
boolean res = true;
for (int i = 1; i < haltingWindow; i++) {
// if historic[-hW] is worse than historic[-hW+i] return false
Double historicIter = bests.get(i);
// if the iterated value (the later one in history) has improved, there is no convergence.
boolean improvementHappened =
(historicIter < historicHWAgo); // testSecondForImprovement(historicHWAgo, historicIter));
if (improvementHappened) {
res = false;
break;
}
}
return res;
}
/**
* This method takes a population of individuals with an array of fitness values and calculates a
* single fitness value to replace the former fitness array. Please note: The orignal fitness
* values are lost this way, so please use the individual.setData() method if you still want to
* access the original fitness values.
*
* @param pop The population to process.
*/
@Override
public void convertMultiObjective2SingleObjective(Population pop) {
for (int i = 0; i < pop.size(); i++) {
this.convertSingleIndividual(pop.get(i));
}
}