@Override
public Boolean f(PopulationBasedAlgorithm a, PopulationBasedAlgorithm b) {
Particle p1 =
(Particle) Topologies.getBestEntity(a.getTopology(), new SocialBestFitnessComparator());
Particle p2 =
(Particle) Topologies.getBestEntity(b.getTopology(), new SocialBestFitnessComparator());
Vector v1 = ((Vector) p1.getBestPosition()).subtract((Vector) p1.getCandidateSolution());
Vector v2 = ((Vector) p2.getBestPosition()).subtract((Vector) p2.getCandidateSolution());
return v1.dot(v2) > 0;
}
/**
* Returns the new position
*
* @param particle The particle to update
* @return the particle's new position
*/
@Override
public Vector get(Particle particle) {
particle.setPositionProvider(new LinearPositionProvider());
Vector solution = (Vector) particle.getCandidateSolution();
Vector pBest = (Vector) particle.getBestPosition();
Vector nBest = (Vector) particle.getNeighbourhoodBest().getBestPosition();
Set<Vector> solutions = new HashSet<Vector>(Arrays.asList(solution, pBest, nBest));
if (solutions.size() == 3) {
return applyCrossover(particle, Lists.newLinkedList(solutions), mainCrossover);
}
Vector prevPos = (Vector) particle.getProperties().get(EntityType.PREVIOUS_SOLUTION);
solutions.add(prevPos);
if (solutions.size() == 3) {
return applyCrossover(particle, Lists.newLinkedList(solutions), mainCrossover);
}
if (solutions.size() == 2) {
return applyCrossover(particle, Lists.newLinkedList(solutions), alternateCrossover);
}
particle.setPositionProvider(new StandardPositionProvider());
return delegate.get(particle);
}