/** {@inheritDoc} */
@Override
public void absorb(Niche algorithm) {
for (PopulationBasedAlgorithm pba : algorithm.getPopulations()) {
RadiusVisitor radiusVisitor = new RadiusVisitor();
pba.accept(radiusVisitor);
double radius = radiusVisitor.getResult().doubleValue();
Topology<? extends Entity> mainSwarmTopology = algorithm.getMainSwarm().getTopology();
for (int i = 0; i < mainSwarmTopology.size(); i++) {
Entity entity = mainSwarmTopology.get(i);
double distance =
distanceMeasure.distance(
entity.getCandidateSolution(),
Topologies.getBestEntity(pba.getTopology()).getCandidateSolution());
if (distance <= radius) {
Particle p = (Particle) entity;
p.setVelocityProvider(new GCVelocityProvider());
p.setNeighbourhoodBest((Particle) Topologies.getBestEntity(pba.getTopology()));
Topology<Particle> topology = (Topology<Particle>) pba.getTopology();
topology.add(p);
algorithm.getMainSwarm().getTopology().remove(entity);
}
}
}
}
/*
* Adds the data patterns closest to a centrid to its data pattern list
* @param candidateSolution The solution holding all the centroids
* @param dataset The dataset holding all the data patterns
*/
public void assignDataPatternsToParticle(CentroidHolder candidateSolution, DataTable dataset) {
double euclideanDistance;
Vector addedPattern;
DistanceMeasure aDistanceMeasure = new EuclideanDistanceMeasure();
for (int i = 0; i < dataset.size(); i++) {
euclideanDistance = Double.POSITIVE_INFINITY;
addedPattern = Vector.of();
Vector pattern = ((StandardPattern) dataset.getRow(i)).getVector();
int centroidIndex = 0;
int patternIndex = 0;
for (ClusterCentroid centroid : candidateSolution) {
if (aDistanceMeasure.distance(centroid.toVector(), pattern) < euclideanDistance) {
euclideanDistance = aDistanceMeasure.distance(centroid.toVector(), pattern);
addedPattern = Vector.copyOf(pattern);
patternIndex = centroidIndex;
}
centroidIndex++;
}
candidateSolution.get(patternIndex).addDataItem(euclideanDistance, addedPattern);
}
}
private void createNiches() {
calculateNeighbourhoodBest();
double smallR = 1000000000000000.0;
double radius;
for (VBParticle p : mainSwarm.getTopology()) {
p.setNeighbourhoodBest(yhead.getClone());
p.calculateVG();
p.calculateDotProduct();
radius = distanceMeaser.distance(yhead.getBestPosition(), p.getPosition());
p.setRadius(radius);
if (radius < smallR && p.getDotProduct() < 0) {
smallR = radius;
}
}
nicheRadius = smallR;
calculateNewNiche();
}
private void merge() {
if (subPopulationsAlgorithms.size() < 2) return;
VBPSO subi, subj;
double d1, d2;
VBParticle yi, yj, swap;
for (int i = 0; i < subPopulationsAlgorithms.size() - 1; i++) {
subi = (VBPSO) subPopulationsAlgorithms.get(i);
yi = (VBParticle) subi.getTopology().get(0).getNeighbourhoodBest();
for (int j = i + 1; j < subPopulationsAlgorithms.size(); j++) {
subj = (VBPSO) subPopulationsAlgorithms.get(j);
yj = (VBParticle) subj.getTopology().get(0).getNeighbourhoodBest();
d1 = distanceMeaser.distance(yi.getBestPosition(), yj.getBestPosition());
if (d1 < granularity) {
if (yi.getBestFitness().compareTo(yj.getBestFitness()) >= 0.0) {
int n = subj.getTopology().size();
VBParticle p;
if (n == 1) {
swap = subj.getTopology().get(0).getClone();
swap.setNeighbourhoodBest(yi);
swap.setNicheID(i);
swap.calculateVP();
swap.calculateVG();
swap.calculateDotProduct();
subi.getTopology().add(swap);
subj.getTopology().remove(0);
} else {
for (int k = 0; k < n; k++) {
p = subj.getTopology().get(k);
d2 = distanceMeaser.distance(p.getBestPosition(), yi.getBestPosition());
if (d2 < granularity) {
swap = p.getClone();
swap.setNeighbourhoodBest(yi);
swap.setNicheID(i);
swap.calculateVP();
swap.calculateVG();
swap.calculateDotProduct();
subi.getTopology().add(swap);
subj.getTopology().remove(k);
k--;
n--;
} else if (n == 1) {
swap = subj.getTopology().get(0).getClone();
swap.setNeighbourhoodBest(yi);
swap.setNicheID(i);
swap.calculateVP();
swap.calculateVG();
swap.calculateDotProduct();
subi.getTopology().add(swap);
subj.getTopology().remove(0);
break;
}
}
}
} else {
int n = subi.getTopology().size();
VBParticle p;
if (n == 1) {
swap = subi.getTopology().get(0);
swap.setNeighbourhoodBest(yj);
swap.setNicheID(j);
swap.calculateVP();
swap.calculateVG();
swap.calculateDotProduct();
subj.getTopology().add(swap);
subi.getTopology().remove(0);
} else {
for (int k = 0; k < n; k++) {
p = subi.getTopology().get(k);
d2 = distanceMeaser.distance(p.getPosition(), yj.getBestPosition());
if (d2 < granularity) {
swap = p.getClone();
swap.setNeighbourhoodBest(yj);
swap.setNicheID(j);
swap.calculateVP();
swap.calculateVG();
swap.calculateDotProduct();
subj.getTopology().add(swap);
subi.getTopology().remove(k);
k--;
n--;
} else if (n == 1) {
swap = subi.getTopology().get(0);
swap.setNeighbourhoodBest(yj);
swap.setNicheID(j);
swap.calculateVP();
swap.calculateVG();
swap.calculateDotProduct();
subj.getTopology().add(swap);
subi.getTopology().remove(0);
}
}
}
}
}
subPopulationsAlgorithms.set(j, subj);
if (subj.getTopology().size() == 0) {
subPopulationsAlgorithms.remove(j);
j--;
}
}
subPopulationsAlgorithms.set(i, subi);
if (subi.getTopology().size() == 0) {
subPopulationsAlgorithms.remove(i);
i--;
}
}
}