/**
* Structure of Dynamic Heterogeneous iteration strategy:
*
* <ol>
* <li>For each particle:
* <li>Check if particle must change its behavior
* <li>If particle must change its behavior:
* <ol>
* <li>Select a new behavior to the particle from the behavior pool
* </ol>
* <li>Perform normal iteration
* </ol>
*
* @see
* net.sourceforge.cilib.pso.iterationstrategies.SynchronousIterationStrategy#performIteration()
*/
@Override
public void performIteration(PSO algorithm) {
checkState(
behaviorPool.size() > 0, "You must add particle behaviors to the behavior pool first.");
for (Entity e : algorithm.getTopology()) {
Particle p = (Particle) e;
if (detectionStrategy.detect(p)) {
p.setParticleBehavior(behaviorSelectionRecipe.on(behaviorPool).select());
}
}
iterationStrategy.performIteration(algorithm);
}
/** {@inheritDoc} */
@Override
protected void algorithmIteration() {
for (HoneyBee bee : workerBees) {
bee.updatePosition();
if (bestBee == null) {
bestBee = bee.getClone();
} else if (bee.getBestFitness().compareTo(bestBee.getBestFitness()) > 0) {
bestBee = bee.getClone();
}
}
for (HoneyBee bee : onlookerBees) {
HoneyBee selectedBee = dancingSelectionStrategy.on(workerBees).select();
bee.setPosition(Vector.copyOf((Vector) selectedBee.getPosition()));
bee.updatePosition();
if (bestBee == null) {
bestBee = bee;
} else if (bee.getBestFitness().compareTo(bestBee.getBestFitness()) > 0) {
bestBee = bee;
}
}
}
/**
* Perform an iteration of the DE algorithm defined as the DE/x/y/z implementation but including a
* parameter adaptation step.
*
* @param ec The {@linkplain EC} on which to perform this iteration.
*/
@Override
public void performIteration(EC ec) {
Topology<SaDEIndividual> topology = (Topology<SaDEIndividual>) ec.getTopology();
for (int i = 0; i < topology.size(); i++) {
SaDEIndividual current = topology.get(i);
current.calculateFitness();
// Create the trial vector by applying mutation
SaDEIndividual targetEntity =
(SaDEIndividual) targetVectorSelectionStrategy.on(topology).exclude(current).select();
// Create the trial vector / entity
SaDEIndividual trialEntity =
(SaDEIndividual)
current.getTrialVectorCreationStrategy().create(targetEntity, current, topology);
// Create the offspring by applying cross-over
List<SaDEIndividual> offspring =
(List<SaDEIndividual>)
current
.getCrossoverStrategy()
.crossover(Arrays.asList(current, trialEntity)); // Order is VERY important here!!
// Replace the parent (current) if the offspring is better
SaDEIndividual offspringEntity = offspring.get(0);
boundaryConstraint.enforce(offspringEntity);
offspringEntity.calculateFitness();
if (offspringEntity.getFitness().compareTo(current.getFitness())
> 0) { // the trial vector is better than the parent
topology.set(i, offspringEntity); // Replace the parent with the offspring individual
}
topology.get(i).updateParameters();
}
}
