/** {@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);
}
}
}
}
/** {@inheritDoc} */
@Override
public Vector get(Particle particle) {
Vector localGuide = (Vector) particle.getLocalGuide();
Vector globalGuide = (Vector) particle.getGlobalGuide();
PSO pso = (PSO) AbstractAlgorithm.get();
List<Entity> positions = getRandomParentEntities(pso.getTopology());
// select three random individuals, all different and different from particle
ProbabilityDistributionFuction pdf = new UniformDistribution();
Vector position1 = (Vector) positions.get(0).getCandidateSolution();
Vector position2 = (Vector) positions.get(1).getCandidateSolution();
// Vector position3 = (Vector) positions.get(2).getContents();
Vector.Builder builder = Vector.newBuilder();
for (int i = 0; i < particle.getDimension(); ++i) {
double r = pdf.getRandomNumber(0, 1);
double attractor = r * localGuide.doubleValueOf(i) + (1 - r) * globalGuide.doubleValueOf(i);
double stepSize =
this.rand3.getRandomNumber(0, 1)
* (position1.doubleValueOf(i) - position2.doubleValueOf(i));
if (this.rand2.getRandomNumber(0, 1) > this.crossoverProbability.getParameter()) {
builder.add(attractor + stepSize);
} else {
builder.add(((Vector) particle.getPosition()).doubleValueOf(i)); // position3.getReal(i));
}
}
return builder.build();
}
private Particle createParticle(Vector vector) {
Particle particle = new StandardParticle();
particle.getProperties().put(EntityType.CANDIDATE_SOLUTION, vector);
particle.getProperties().put(EntityType.Particle.VELOCITY, Vector.of(0.0));
particle.getProperties().put(EntityType.Particle.BEST_POSITION, vector.getClone());
return particle;
}
/**
* Structure of Dynamic Heterogeneous iteration strategy:
*
* <ol>
* <li>For each particle:
* <li>Check if particle must change its behavior
* <li>If particle must change its behavior:
* <ol>
* <li>Select a new behavior to the particle from the behavior pool
* </ol>
* <li>Perform normal iteration
* </ol>
*
* @see
* net.sourceforge.cilib.pso.iterationstrategies.SynchronousIterationStrategy#performIteration()
*/
@Override
public void performIteration(PSO algorithm) {
checkState(
behaviorPool.size() > 0, "You must add particle behaviors to the behavior pool first.");
for (Entity e : algorithm.getTopology()) {
Particle p = (Particle) e;
if (detectionStrategy.detect(p)) {
p.setParticleBehavior(behaviorSelectionRecipe.on(behaviorPool).select());
}
}
iterationStrategy.performIteration(algorithm);
}
/** Test velocity clamping. */
@Test
public void testClamping() {
Particle particle = createParticle(Vector.of(0.0));
Particle nBest = createParticle(Vector.of(1.0));
particle.setNeighbourhoodBest(nBest);
nBest.setNeighbourhoodBest(nBest);
ClampingVelocityProvider velocityProvider = new ClampingVelocityProvider();
velocityProvider.setVMax(new ConstantControlParameter(0.5));
Vector velocity = velocityProvider.get(particle);
for (Numeric number : velocity) {
Assert.assertTrue(Double.compare(number.doubleValue(), 0.5) <= 0);
Assert.assertTrue(Double.compare(number.doubleValue(), -0.5) >= 0);
}
}
/** {@inheritDoc} */
public Real getValue(Algorithm algorithm) {
PSO pso = (PSO) algorithm;
int numberParticles = pso.getTopology().size();
Iterator<Particle> k = pso.getTopology().iterator();
Particle particle = k.next();
Vector averageParticlePosition = (Vector) particle.getPosition().getClone();
while (k.hasNext()) {
particle = k.next();
Vector v = (Vector) particle.getPosition();
for (int j = 0; j < averageParticlePosition.size(); ++j) {
averageParticlePosition.setReal(
j, averageParticlePosition.doubleValueOf(j) + v.doubleValueOf(j));
}
}
for (int j = 0; j < averageParticlePosition.size(); ++j) {
averageParticlePosition.setReal(
j, averageParticlePosition.doubleValueOf(j) / numberParticles);
}
Iterator<Particle> i = pso.getTopology().iterator();
double particleSum = 0.0;
while (i.hasNext()) {
particle = i.next();
double dimensionSum = 0.0;
Vector v = (Vector) particle.getPosition();
for (int j = 0; j < particle.getDimension(); ++j) {
dimensionSum +=
(v.doubleValueOf(j) - averageParticlePosition.doubleValueOf(j))
* (v.doubleValueOf(j) - averageParticlePosition.doubleValueOf(j));
}
particleSum += Math.sqrt(dimensionSum);
}
double diversity = particleSum / numberParticles;
DiameterVisitor diameterVisitor = new DiameterVisitor();
pso.accept(diameterVisitor);
double diameter = diameterVisitor.getResult();
return Real.valueOf(diversity / diameter);
}
private Vector applyCrossover(
Particle particle, List<Vector> parents, CrossoverStrategy crossover) {
List<Entity> entityParents = Lists.newLinkedList();
for (Vector v : parents) {
Entity parent = particle.getClone();
parent.setCandidateSolution(v);
entityParents.add(parent);
}
return (Vector) crossover.crossover(entityParents).get(0).getCandidateSolution();
}
/**
* 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);
}
@Test
public void initialize() {
final Bounds bounds = new Bounds(-8.0, 8.0);
Vector vector =
Vector.newBuilder()
.addWithin(0.0, bounds)
.addWithin(0.0, bounds)
.addWithin(0.0, bounds)
.build();
Particle particle = new StandardParticle();
particle.getProperties().put(EntityType.Particle.VELOCITY, vector);
DomainPercentageInitializationStrategy<Particle> strategy =
new DomainPercentageInitializationStrategy<Particle>();
strategy.setVelocityInitialisationStrategy(new ConstantInitializationStrategy(1.0));
strategy.initialize(EntityType.Particle.VELOCITY, particle);
Vector velocity = (Vector) particle.getVelocity();
for (int i = 0; i < velocity.size(); i++) {
Assert.assertThat(velocity.doubleValueOf(i), is(lessThanOrEqualTo(0.1)));
}
}
@Override
public Vector get(Particle particle) {
Vector localGuide = (Vector) particle.getLocalGuide();
Vector globalGuide = (Vector) particle.getGlobalGuide();
Vector.Builder builder = Vector.newBuilder();
for (int i = 0; i < particle.getDimension(); ++i) {
if (this.uniform.getRandomNumber(0, 1) < 0.5) {
builder.add(localGuide.doubleValueOf(i));
} else {
// double tmp1 = cognitive.getParameter();
// double tmp2 = social.getParameter();
double sigma = Math.abs(localGuide.doubleValueOf(i) - globalGuide.doubleValueOf(i));
// according to Kennedy
double mean = (localGuide.doubleValueOf(i) + globalGuide.doubleValueOf(i)) / 2;
// andries proposal: double mean = (tmp1*personalBestPosition.getReal(i) +
// tmp2*nBestPosition.getReal(i)) / (tmp1+tmp2);
builder.add(this.randomDistribution.getRandomNumber(mean, sigma));
}
}
return builder.build();
}
@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;
}
/** {@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);
// }
}
@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());
}
}
@Override
public void updateGuide(Particle particle, EntityType.Particle.Guide guideType, Vector newGuide) {
particle.getProperties().put(guideType, newGuide);
}
