/** {@inheritDoc} */
@Override
public <T extends Entity> T create(T targetEntity, T current, Topology<T> topology) {
T bestEntity = Topologies.getBestEntity(topology);
List<T> participants =
Selection.copyOf(topology)
.exclude(targetEntity, bestEntity, current)
.orderBy(new RandomArrangement())
.select(Samples.first((int) numberOfDifferenceVectors.getParameter()).unique());
Vector differenceVector = determineDistanceVector(participants);
Vector targetVector =
((Vector) targetEntity.getCandidateSolution())
.multiply(1 - greedynessParameter.getParameter());
Vector bestVector =
((Vector) bestEntity.getCandidateSolution()).multiply(greedynessParameter.getParameter());
Vector trialVector =
bestVector.plus(
targetVector.plus(differenceVector.multiply(scaleParameter.getParameter())));
T trialEntity = (T) current.getClone();
trialEntity.setCandidateSolution(trialVector);
return trialEntity;
}
/** {@inheritDoc} */
@Override
public <T extends Entity> T create(T targetEntity, T current, fj.data.List<T> topology) {
int number = Double.valueOf(this.numberOfDifferenceVectors.getParameter()).intValue();
List<T> participants =
Selection.copyOf(topology)
.exclude(targetEntity, current)
.orderBy(new RandomArrangement())
.select(Samples.first(number).unique());
Vector differenceVector = determineDistanceVector(participants);
Vector targetVector = (Vector) targetEntity.getCandidateSolution();
Vector trialVector =
targetVector.plus(
differenceVector.multiply(
new P1<Number>() {
@Override
public Number _1() {
return scaleParameter.getParameter();
}
}));
T trialEntity = (T) current.getClone();
trialEntity.setCandidateSolution(trialVector);
return trialEntity;
}
@Override
public void performIteration(PSO algorithm) {
delegate.performIteration(algorithm);
Topology<Particle> topology = algorithm.getTopology();
// calculate vAvg
Vector avgV =
Vectors.mean(
Lists.transform(
topology,
new Function<Particle, Vector>() {
@Override
public Vector apply(Particle f) {
return (Vector) f.getVelocity();
}
}));
Vector.Builder builder = Vector.newBuilder();
for (Numeric n : avgV) {
if (Math.abs(n.doubleValue()) > vMax.getParameter()) {
builder.add(vMax.getParameter());
} else {
builder.add(n);
}
}
avgV = builder.build();
// mutation
Particle gBest = Topologies.getBestEntity(topology, new SocialBestFitnessComparator());
Particle mutated = gBest.getClone();
Vector pos = (Vector) gBest.getBestPosition();
final Bounds bounds = pos.boundsOf(0);
pos =
pos.plus(
avgV.multiply(
new Supplier<Number>() {
@Override
public Number get() {
return distribution.getRandomNumber() * bounds.getRange()
+ bounds.getLowerBound();
}
}));
mutated.setCandidateSolution(pos);
mutated.calculateFitness();
if (gBest.getBestFitness().compareTo(mutated.getFitness()) < 0) {
gBest.getProperties().put(EntityType.Particle.BEST_FITNESS, mutated.getBestFitness());
gBest.getProperties().put(EntityType.Particle.BEST_POSITION, mutated.getBestPosition());
}
}
/** {@inheritDoc} */
@Override
public Entity create(Entity targetEntity, Entity current, Topology<? extends Entity> topology) {
List<Entity> participants =
(List<Entity>)
Selection.copyOf(topology).exclude(targetEntity, current).select(Samples.all());
Vector differenceVector = determineDistanceVector(participants);
Vector targetVector = (Vector) targetEntity.getCandidateSolution();
Vector trialVector =
targetVector.plus(
differenceVector.multiply(
new P1<Number>() {
@Override
public Number _1() {
return scaleParameter.getParameter();
}
}));
Entity trialEntity = current.getClone();
trialEntity.setCandidateSolution(trialVector);
return trialEntity;
}
