/**
* Initializes the CMA parameter set for given mu, lambda and a population. The initialSigma
* parameter is used as initial sigma directly unless it is <0, in that case the average range is
* used as initial sigma. The parameter instance is also added as listener to the population.
*
* @param params the CMA parameter set to be used - its data are overwritten
* @param mu ES mu parameter
* @param lambda ES lambda parameter
* @param pop associated Population
* @param initialSigma initial sigma or -1 to indicate the usage of average range
* @return
*/
public static CMAParamSet initCMAParams(
CMAParamSet params, int mu, int lambda, Population pop, double initialSigma) {
initCMAParams(
params,
mu,
lambda,
pop.getBestEAIndividual().getDoublePosition(),
((InterfaceDataTypeDouble) pop.getEAIndividual(0)).getDoubleRange(),
initialSigma);
pop.addPopulationChangedEventListener(params);
return params;
}
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;
}
/**
* 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;
}
