/** {@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;
}
