private void calculateNewNiche() {
setCurrentNiche(getCurrentNiche() + 1);
VBPSO sub = mainSwarm.getClone();
sub.getTopology().clear();
yhead.setNicheID(getCurrentNiche());
yhead.setNeighbourhoodBest(yhead.getClone());
sub.getTopology().add(yhead);
for (VBParticle p : mainSwarm.getTopology()) {
if ((p.getDotProduct() > 0 && p.getRadius() < nicheRadius)
|| (p.getBestPosition().equals(yhead.getBestPosition()))) {
p.setNicheID(getCurrentNiche());
p.setNeighbourhoodBest(yhead.getClone());
sub.getTopology().add(p);
}
}
for (VBParticle p : sub.getTopology()) {
mainSwarm.getTopology().remove(p);
}
VBParticle tmp = yhead.getClone();
while (sub.getTopology().size() < 3) {
tmp.reinitialise();
tmp.setNeighbourhoodBest(yhead.getClone());
tmp.setPersonalBest(tmp.getPosition().getClone());
sub.getTopology().add(tmp);
}
subPopulationsAlgorithms.add(sub);
}
private void updateVPs() {
if (this.getIterations() == 0) {
try {
FunctionMaximisationProblem prob =
(FunctionMaximisationProblem) this.getOptimisationProblem();
function = (ContinuousFunction) prob.getFunction();
isMax = true;
} catch (ClassCastException e) {
FunctionMinimisationProblem prob =
(FunctionMinimisationProblem) this.getOptimisationProblem();
function = (ContinuousFunction) prob.getFunction();
isMax = false;
} finally {
double s;
for (VBParticle p : mainSwarm.getTopology()) {
p.calculateFitness();
Vector random = (Vector) p.getPosition().getClone();
random.randomize(randomProvider);
s = function.apply(random);
if (isMax) {
if (s > p.getFitness().getValue()) {
p.setPersonalBest(random);
} else {
p.setPersonalBest(p.getPosition());
p.setPosition(random);
}
} else {
if (s < p.getFitness().getValue()) {
p.setPersonalBest(random);
} else {
p.setPersonalBest(p.getPosition());
p.setPosition(random);
}
}
p.setNeighbourhoodBest(p.getClone());
p.calculateVP();
}
}
}
}
