/** {@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());
}
}
/**
* Calculate the {@linkplain Vector} that is the resultant of several difference vectors.
*
* @param participants The {@linkplain Entity} list to create the difference vectors from. It is
* very important to note that the {@linkplain Entity} objects within this list should not
* contain duplicates. If duplicates are contained, this will severely reduce the diversity of
* the population as not all entities will be considered.
* @return A {@linkplain Vector} representing the resultant of all calculated difference vectors.
*/
protected <T extends Entity> Vector determineDistanceVector(List<T> participants) {
Vector distanceVector = Vector.fill(0.0, participants.get(0).getCandidateSolution().size());
Iterator<T> iterator = participants.iterator();
while (iterator.hasNext()) {
Vector first = (Vector) iterator.next().getCandidateSolution();
Vector second = (Vector) iterator.next().getCandidateSolution();
Vector difference = first.subtract(second);
distanceVector = distanceVector.plus(difference);
}
return distanceVector;
}
@Override
public Vector get(Particle particle) {
Vector newPos = (Vector) delegate.get(particle);
Particle tmp = particle.getClone();
tmp.setPosition(newPos);
Fitness newFitness = particle.getBehaviour().getFitnessCalculator().getFitness(tmp);
final UniformDistribution uniform = new UniformDistribution();
Vector newPBest =
newPos.plus(
Vector.newBuilder()
.repeat(newPos.size(), Real.valueOf(1.0))
.build()
.multiply(
new P1<Number>() {
@Override
public Number _1() {
return uniform.getRandomNumber(
-granularity.getParameter(), granularity.getParameter());
}
}));
tmp.setPosition(newPos);
Fitness newPBestFitness = particle.getBehaviour().getFitnessCalculator().getFitness(tmp);
if (newPBestFitness.compareTo(newFitness) < 0) {
Vector tmpVector = Vector.copyOf(newPos);
newPos = newPBest;
newPBest = tmpVector;
newPBestFitness = newFitness;
}
double dot =
((Vector) particle.getNeighbourhoodBest().getBestPosition())
.subtract(newPos)
.dot(newPBest.subtract(newPos));
if (dot < 0) {
return (Vector) particle.getPosition();
}
particle.put(Property.BEST_POSITION, newPBest);
particle.put(Property.BEST_FITNESS, newPBestFitness);
return newPos;
}
/** {@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;
}
/** {@inheritDoc} */
@Override
public Vector get(Particle particle) {
double averageParticleVelocity = 0.0;
Vector averageVelocity = null; // velocity.getClone();
// averageVelocity.reset();
PSO pso = (PSO) AbstractAlgorithm.get();
for (Particle p : pso.getTopology()) {
if (averageVelocity == null) {
averageVelocity = (Vector) p.getVelocity();
continue;
}
Vector particleVelocity = (Vector) p.getVelocity();
averageVelocity = averageVelocity.plus(particleVelocity);
averageParticleVelocity += particleVelocity.norm();
}
averageVelocity = averageVelocity.divide(particle.getDimension());
averageParticleVelocity /= particle.getDimension();
double swarmCenterVelocity = averageVelocity.norm();
double swarmCoherence = calculateSwarmCoherence(swarmCenterVelocity, averageParticleVelocity);
double sigmoidValue = this.sigmoid.apply(swarmCoherence);
Vector standardVelocity = this.delegate.get(particle);
Vector.Builder builder = Vector.newBuilder();
for (int i = 0; i < particle.getDimension(); ++i) {
double coherenceVelocity =
this.scalingFactor.getParameter()
* sigmoidValue
* averageVelocity.doubleValueOf(i)
* this.randomNumber.getRandomNumber();
builder.add(coherenceVelocity);
}
Vector coherence = builder.build();
return Vectors.sumOf(standardVelocity, coherence);
// float social = socialRandomGenerator.nextFloat();
// float cognitive = cognitiveRandomGenerator.nextFloat();
//
// //DistanceMeasure adm = new AbsoluteDistanceMeasure();
// //DistanceMeasure dm = new MetricDistanceMeasure();
//
// double avgv = 0.0;
// double swv = 0.0;
// Topology<Particle> topology = ((PSO)Algorithm.get()).getTopology();
// Iterator<? extends Particle> it = topology.neighbourhood(null);
// double[] al = new double[particle.getDimension()];
// while (it.hasNext()) {
// Particle pl = it.next();
// double tmpv = 0.0;
// //double tmpsv = 0.0;
// for(int dim = 0; dim < particle.getDimension(); dim++) {
// al[dim] = al[dim]+((Vector)pl.getVelocity()).getReal(dim);
// tmpv += Math.pow(((Vector)pl.getVelocity()).getReal(dim), 2);
// }
// tmpv = Math.sqrt(tmpv);
// avgv += tmpv;
// }
// for(int i = 0; i < particle.getDimension(); i++) {
// //al.set(i, ;
// swv += (al[i]/topology.size()) * (al[i]/topology.size());
// }
// swv = Math.sqrt(swv);
//
// for (int i = 0; i < particle.getDimension(); ++i) {
// double tmp = 0.0;
// tmp = inertiaWeight.getParameter()*velocity.getReal(i)
// + cognitive * (bestPosition.getReal(i) - position.getReal(i)) *
// cognitiveAcceleration.getParameter()
// + social * (nBestPosition.getReal(i) - position.getReal(i)) *
// socialAcceleration.getParameter();
//
// double avgdim = 0.0;
// it = topology.neighbourhood(null);
// while (it.hasNext()) {
// avgdim += ((Vector)(it.next().getVelocity())).getReal(i);
// }
// avgdim /= particle.getDimension();
//
// double cvelocity = MathUtil.sigmoid(swv/avgv)*avgdim*randomNumber.getCauchy();
//
// System.out.println(cvelocity);
// tmp += cvelocity;
//
// velocity.setReal(i, tmp);
//
// clamp(velocity, i);
// }
}
/** {@inheritDoc} */
@Override
public <E extends Entity> List<E> crossover(List<E> parentCollection) {
checkState(
parentCollection.size() >= 3,
"There must be a minimum of three parents to perform UNDX crossover.");
checkState(
numberOfOffspring > 0,
"At least one offspring must be generated. Check 'numberOfOffspring'.");
List<Vector> solutions = Entities.<Vector>getCandidateSolutions(parentCollection);
List<E> offspring = Lists.newArrayListWithCapacity(numberOfOffspring);
UniformDistribution randomParent = new UniformDistribution();
final int k = solutions.size();
final int n = solutions.get(0).size();
for (int os = 0; os < numberOfOffspring; os++) {
// get index of main parent and put its solution at the end of the list
int parent = (int) randomParent.getRandomNumber(0.0, k);
Collections.swap(solutions, parent, k - 1);
List<Vector> e_zeta = new ArrayList<Vector>();
// calculate mean of parents except main parent
Vector g = Vectors.mean(solutions.subList(0, k - 1));
// basis vectors defined by parents
for (int i = 0; i < k - 1; i++) {
Vector d = solutions.get(i).subtract(g);
if (!d.isZero()) {
double dbar = d.length();
Vector e = d.orthogonalize(e_zeta);
if (!e.isZero()) {
e_zeta.add(e.normalize().multiply(dbar));
}
}
}
final double D = solutions.get(k - 1).subtract(g).length();
// create the remaining basis vectors
List<Vector> e_eta = Lists.newArrayList();
e_eta.add(solutions.get(k - 1).subtract(g));
for (int i = 0; i < n - e_zeta.size() - 1; i++) {
Vector d = Vector.newBuilder().copyOf(g).buildRandom();
e_eta.add(d);
}
e_eta = Vectors.orthonormalize(e_eta);
// construct the offspring
Vector variables = Vector.copyOf(g);
if (!useIndividualProviders) {
for (int i = 0; i < e_zeta.size(); i++) {
variables =
variables.plus(
e_zeta.get(i).multiply(random.getRandomNumber(0.0, sigma1.getParameter())));
}
for (int i = 0; i < e_eta.size(); i++) {
variables =
variables.plus(
e_eta
.get(i)
.multiply(
D * random.getRandomNumber(0.0, sigma2.getParameter() / Math.sqrt(n))));
}
} else {
for (int i = 0; i < e_zeta.size(); i++) {
variables =
variables.plus(
e_zeta
.get(i)
.multiply(
new Supplier<Number>() {
@Override
public Number get() {
return random.getRandomNumber(0.0, sigma1.getParameter());
}
}));
}
for (int i = 0; i < e_eta.size(); i++) {
variables =
variables.plus(
e_eta
.get(i)
.multiply(
new Supplier<Number>() {
@Override
public Number get() {
return D
* random.getRandomNumber(
0.0, sigma2.getParameter() / Math.sqrt(n));
}
}));
}
}
E child = (E) parentCollection.get(parent).getClone();
child.setCandidateSolution(variables);
offspring.add(child);
}
return offspring;
}
