private void calculateAbsoluteAverages(PSO pso) {
int dimension = pso.getTopology().last().getDimension();
absoluteAverageVelocityVector = Vector.of();
averageSpeedVector = Vector.of();
for (Entity e : pso.getTopology()) {
Vector velocity = (Vector) e.getProperties().get(EntityType.Particle.VELOCITY);
for (int i = 0; i < dimension; i++) {
if (absoluteAverageVelocityVector.size() < dimension) {
absoluteAverageVelocityVector.add(velocity.get(i));
averageSpeedVector.add(Real.valueOf(Math.abs(velocity.doubleValueOf(i))));
} else {
absoluteAverageVelocityVector.setReal(
i, absoluteAverageVelocityVector.doubleValueOf(i) + velocity.doubleValueOf(i));
averageSpeedVector.setReal(
i, averageSpeedVector.doubleValueOf(i) + Math.abs(velocity.doubleValueOf(i)));
}
}
}
for (int i = 0; i < dimension; i++) {
absoluteAverageVelocityVector.setReal(
i, Math.abs(absoluteAverageVelocityVector.doubleValueOf(i) / (double) dimension));
averageSpeedVector.setReal(i, averageSpeedVector.doubleValueOf(i) / (double) dimension);
}
}
@Override
public Vector get(Particle particle) {
Vector velocity = (Vector) particle.getVelocity();
Vector position = (Vector) particle.getPosition();
PSO algorithm = (PSO) AbstractAlgorithm.get();
int ns = (int) nSize.getParameter();
fj.data.List<Particle> neighbours =
algorithm
.getTopology()
.sort(
Ord.ord(
VectorBasedFunctions.sortByDistance(particle, new EuclideanDistanceMeasure())))
.take(ns);
Vector.Builder builder = Vector.newBuilder();
for (int i = 0; i < particle.getDimension(); ++i) {
double informationSum = 0.0;
double randomSum = 0;
for (Particle currentTarget : neighbours) {
Vector currentTargetPosition = (Vector) currentTarget.getBestPosition();
double randomComponent = Rand.nextDouble() * (4.1 / ns);
informationSum += randomComponent * currentTargetPosition.doubleValueOf(i);
randomSum += randomComponent;
}
double value =
inertiaWeight.getParameter()
* (velocity.doubleValueOf(i)
+ randomSum * ((informationSum / (ns * randomSum) - position.doubleValueOf(i))));
builder.add(value);
}
return builder.build();
}
@Test
public void testCreation() {
Particle p1 =
NichingFunctionsTest.createParticle(new MinimisationFitness(3.0), Vector.of(0.0, 1.0));
Particle p2 =
NichingFunctionsTest.createParticle(new MinimisationFitness(2.0), Vector.of(1.0, 1.0));
Particle p3 =
NichingFunctionsTest.createParticle(new MinimisationFitness(1.0), Vector.of(2.0, 2.0));
PSO pso = new PSO();
pso.getTopology().addAll(Arrays.asList(p1, p2, p3));
ClosestNeighbourNicheCreationStrategy creator = new ClosestNeighbourNicheCreationStrategy();
creator.setSwarmBehavior(new ParticleBehavior());
NichingSwarms swarms =
creator.f(NichingSwarms.of(pso, List.<PopulationBasedAlgorithm>nil()), p1);
Assert.assertEquals(1, swarms._1().getTopology().size());
Assert.assertEquals(
Vector.of(2.0, 2.0), swarms._1().getTopology().get(0).getCandidateSolution());
Assert.assertEquals(2, swarms._2().head().getTopology().size());
Assert.assertEquals(
Vector.of(0.0, 1.0), swarms._2().head().getTopology().get(0).getCandidateSolution());
Assert.assertEquals(
Vector.of(1.0, 1.0), swarms._2().head().getTopology().get(1).getCandidateSolution());
}
/** {@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();
}
@Override
public Topology<Particle> f(PSO pso) {
Topology<Particle> newTopology = pso.getTopology().getClone();
newTopology.clear();
for (Particle p : pso.getTopology()) {
newTopology.add(function.f(p));
}
return newTopology;
}
@Test
public void value() {
PSO pso = new PSO();
MeasuredStoppingCondition maximumIterations =
new MeasuredStoppingCondition(new Iterations(), new Maximum(), 100);
pso.addStoppingCondition(maximumIterations);
for (int i = 0; i < 10; i++) pso.performIteration();
PercentageComplete percentageComplete = new PercentageComplete();
Assert.assertEquals(0.1, percentageComplete.getValue(pso).doubleValue(), 0.001);
}
/** {@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);
}
@Override
public double getPercentageCompleted(PSO algorithm) {
DiameterVisitor diameterVisitor = new DiameterVisitor();
algorithm.accept(diameterVisitor);
double diameter = diameterVisitor.getResult();
if (diameter <= minimumSwarmDiameter) {
return 1;
}
return minimumSwarmDiameter / diameter;
}
@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());
}
}
/** {@inheritDoc} */
@Override
public StringType getValue(Algorithm algorithm) {
final StringBuilder tmp = new StringBuilder();
final PSO pso = (PSO) algorithm;
for (Particle particle : pso.getTopology()) {
tmp.append("\nParticle: ");
tmp.append(" Current Fitness: ");
tmp.append(particle.getFitness().getValue());
tmp.append(" Best Fitness: ");
tmp.append(particle.getBestFitness().getValue());
tmp.append(" Position: ");
Vector v = (Vector) particle.getPosition();
for (int j = 0; j < particle.getDimension(); ++j) {
tmp.append(v.doubleValueOf(j));
tmp.append(" ");
}
}
return new StringType(tmp.toString());
}
/** @param pso The {@link PSO} to have an iteration applied. */
@Override
public void performIteration(final PSO pso) {
final fj.data.List<Particle> topology = pso.getTopology();
this.calculateAbsoluteAverages(pso);
this.updateInertia(pso);
final F<Particle, Particle> first =
new F<Particle, Particle>() {
@Override
public Particle f(Particle current) {
WeightedInertiaVelocityProvider wp =
(WeightedInertiaVelocityProvider) current.getVelocityProvider(); // put
wp.setInertiaWeight(inertiaWeight);
current.updateVelocity();
current.updatePosition();
boundaryConstraint.enforce(current);
return current;
}
};
final F<Particle, Particle> second =
new F<Particle, Particle>() {
public Particle f(Particle current) {
current.calculateFitness();
for (Particle other : pso.getNeighbourhood().f(topology, current)) {
if (current
.getSocialFitness()
.compareTo(other.getNeighbourhoodBest().getSocialFitness())
> 0) {
other.setNeighbourhoodBest(current);
}
}
return current;
}
};
pso.setTopology(topology.map(first).map(second));
}
/**
* 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);
}
/**
* This is an Synchronous strategy:
*
* <ol>
* <li>For all particles:
* <ol>
* <li>Update the particle velocity
* <li>Update the particle position
* </ol>
* <li>For all particles:
* <ol>
* <li>Calculate the particle fitness
* <li>For all particles in the current particle's neighbourhood:
* <ol>
* <li>Update the neighbourhood best
* </ol>
* </ol>
* </ol>
*
* @see
* net.sourceforge.cilib.PSO.IterationStrategy#performIteration(net.sourceforge.cilib.PSO.PSO)
* @param pso The {@link PSO} to have an iteration applied.
*/
@Override
public void performIteration(PSO pso) {
Topology<Particle> topology = pso.getTopology();
for (Particle current : topology) {
current.updateVelocity();
current.updatePosition(); // TODO: replace with visitor (will simplify particle interface)
boundaryConstraint.enforce(current);
}
Problem problem = AbstractAlgorithm.getAlgorithmList().get(0).getOptimisationProblem();
for (Particle current : topology) {
current.calculateFitness();
for (Particle other : topology.neighbourhood(current)) {
Particle p1 = current.getNeighbourhoodBest().getClone();
Particle p2 = other.getNeighbourhoodBest().getClone();
OptimisationSolution s1 =
new OptimisationSolution(
p1.getCandidateSolution().getClone(),
problem.getFitness(p1.getCandidateSolution().getClone()));
OptimisationSolution s2 =
new OptimisationSolution(
p2.getCandidateSolution().getClone(),
problem.getFitness(p2.getCandidateSolution().getClone()));
MOFitness fitness1 = (MOFitness) s1.getFitness();
MOFitness fitness2 = (MOFitness) s2.getFitness();
// System.out.println("fitness1 = ");
// for (int i=0; i < fitness1.getDimension(); i++)
// System.out.println(fitness1.getFitness(i).getValue());
//
// System.out.println("fitness2 = ");
// for (int i=0; i < fitness2.getDimension(); i++)
// System.out.println(fitness2.getFitness(i).getValue());
if (fitness1.compareTo(fitness2) > 0) {
other.setNeighbourhoodBest(current);
}
}
}
}
@Test
public void algorithmExecution() {
NNDataTrainingProblem problem = new NNDataTrainingProblem();
problem.getDataTableBuilder().setDataReader(new ARFFFileReader());
problem.getDataTableBuilder().setSourceURL("library/src/test/resources/datasets/iris.arff");
problem.setTrainingSetPercentage(0.7);
problem.setGeneralizationSetPercentage(0.3);
problem
.getNeuralNetwork()
.getArchitecture()
.setArchitectureBuilder(new CascadeArchitectureBuilder());
problem.getNeuralNetwork().setOperationVisitor(new CascadeVisitor());
problem
.getNeuralNetwork()
.getArchitecture()
.getArchitectureBuilder()
.addLayer(new LayerConfiguration(4));
problem
.getNeuralNetwork()
.getArchitecture()
.getArchitectureBuilder()
.addLayer(new LayerConfiguration(0));
problem
.getNeuralNetwork()
.getArchitecture()
.getArchitectureBuilder()
.addLayer(new LayerConfiguration(1));
problem
.getNeuralNetwork()
.getArchitecture()
.getArchitectureBuilder()
.getLayerBuilder()
.setDomain("R(-3:3)");
problem.initialise();
PSO pso = new PSO();
pso.getInitialisationStrategy().setEntityType(new DynamicParticle());
pso.addStoppingCondition(new MeasuredStoppingCondition());
pso.setOptimisationProblem(problem);
pso.performInitialisation();
CascadeNetworkExpansionReactionStrategy reaction =
new CascadeNetworkExpansionReactionStrategy();
Assert.assertEquals(5, ((Vector) pso.getBestSolution().getPosition()).size());
Assert.assertEquals(5, problem.getNeuralNetwork().getWeights().size());
for (int i = 0; i < Topologies.getBestEntity(pso.getTopology()).getDimension(); ++i) {
((Vector) Topologies.getBestEntity(pso.getTopology()).getPosition())
.set(i, Real.valueOf(0.0));
((Vector) Topologies.getBestEntity(pso.getTopology()).getVelocity())
.set(i, Real.valueOf(0.0));
((Vector) Topologies.getBestEntity(pso.getTopology()).getBestPosition())
.set(i, Real.valueOf(0.0));
}
reaction.performReaction(pso);
Assert.assertEquals(11, ((Vector) pso.getBestSolution().getPosition()).size());
Assert.assertEquals(11, problem.getNeuralNetwork().getWeights().size());
Assert.assertEquals(
Vector.of(
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN),
(Vector) Topologies.getBestEntity(pso.getTopology()).getPosition());
Assert.assertEquals(
Vector.of(
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN),
(Vector) Topologies.getBestEntity(pso.getTopology()).getVelocity());
Assert.assertEquals(
Vector.of(
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN),
(Vector) Topologies.getBestEntity(pso.getTopology()).getBestPosition());
for (int i = 0; i < Topologies.getBestEntity(pso.getTopology()).getDimension(); ++i) {
((Vector) Topologies.getBestEntity(pso.getTopology()).getPosition())
.set(i, Real.valueOf(0.0));
((Vector) Topologies.getBestEntity(pso.getTopology()).getVelocity())
.set(i, Real.valueOf(0.0));
((Vector) Topologies.getBestEntity(pso.getTopology()).getBestPosition())
.set(i, Real.valueOf(0.0));
}
reaction.performReaction(pso);
Assert.assertEquals(18, ((Vector) pso.getBestSolution().getPosition()).size());
Assert.assertEquals(18, problem.getNeuralNetwork().getWeights().size());
Assert.assertEquals(
Vector.of(
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN),
(Vector) Topologies.getBestEntity(pso.getTopology()).getPosition());
Assert.assertEquals(
Vector.of(
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN),
(Vector) Topologies.getBestEntity(pso.getTopology()).getVelocity());
Assert.assertEquals(
Vector.of(
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN),
(Vector) Topologies.getBestEntity(pso.getTopology()).getBestPosition());
for (int i = 0; i < Topologies.getBestEntity(pso.getTopology()).getDimension(); ++i) {
((Vector) Topologies.getBestEntity(pso.getTopology()).getPosition())
.set(i, Real.valueOf(0.0));
((Vector) Topologies.getBestEntity(pso.getTopology()).getVelocity())
.set(i, Real.valueOf(0.0));
((Vector) Topologies.getBestEntity(pso.getTopology()).getBestPosition())
.set(i, Real.valueOf(0.0));
}
reaction.performReaction(pso);
Assert.assertEquals(26, ((Vector) pso.getBestSolution().getPosition()).size());
Assert.assertEquals(26, problem.getNeuralNetwork().getWeights().size());
Assert.assertEquals(
Vector.of(
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN),
(Vector) Topologies.getBestEntity(pso.getTopology()).getPosition());
Assert.assertEquals(
Vector.of(
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN),
(Vector) Topologies.getBestEntity(pso.getTopology()).getVelocity());
Assert.assertEquals(
Vector.of(
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN),
(Vector) Topologies.getBestEntity(pso.getTopology()).getBestPosition());
}
@Override
public boolean apply(PSO input) {
DiameterVisitor diameterVisitor = new DiameterVisitor();
input.accept(diameterVisitor);
return (diameterVisitor.getResult() <= minimumSwarmDiameter);
}
private void updateInertia(PSO pso) {
int dimension = pso.getTopology().last().getDimension();
if (inertiaWeight.size() < dimension) {
Vector.Builder builder = Vector.newBuilder();
builder.repeat(dimension, Real.valueOf(initialInertiaWeight.getParameter()));
inertiaWeight = builder.build();
}
Vector.Builder builder = Vector.newBuilder();
for (int i = 0; i < dimension; i++) {
builder.add(
Math.sqrt(
Math.pow(absoluteAverageVelocityVector.doubleValueOf(i), 2)
+ Math.pow(averageSpeedVector.doubleValueOf(i), 2)));
}
Vector d = builder.build(); // get the degree of convergence vector
double max_d = 0;
for (Numeric component : d) {
if (component.doubleValue() > max_d) {
max_d = component.doubleValue();
}
}
if (max_d != 0) {
Vector.Builder builder2 = Vector.newBuilder();
for (Numeric component : d) {
builder2.add(max_d / (max_d + component.doubleValue()));
}
Vector w = builder2.build();
/*double sum_w = 0;
for(Numeric component : w) {
sum_w += component.doubleValue();
}
/*
Vector.Builder builder3 = Vector.newBuilder();
for(Numeric component : w) {
builder3.add(Math.pow(dimension * component.doubleValue() / sum_w, pwr.getParameter()));
} */
/*
for(Numeric component : w) {
//builder3.add(component.doubleValue() - w_mean / w_stdDiv);
builder3.add(component.doubleValue() * initialInertiaWeight.getParameter());
}
for(int i = 0; i < inertiaWeight.size(); i++) {
builder3.add(w.doubleValueOf(i) * inertiaWeight.doubleValueOf(i));
}
*/
/*
Vector m = builder3.build();
double sum_m = 0;
for (Numeric num : m) {
sum_m += num.doubleValue();
}
double m_mean = sum_m / (double) dimension;
double sum_diff_squared = 0;
for(Numeric component : m) {
sum_diff_squared += Math.pow(component.doubleValue() - m_mean, 2);
}
double m_stdDiv = Math.sqrt(sum_diff_squared / (double) dimension);
*/
// System.out.println("VEL: StdDiv of M: " + m_stdDiv + ", mean of M: " + m_mean);
for (int i = 0; i < inertiaWeight.size(); i++) {
inertiaWeight.setReal(
i,
(1 - filter.getParameter()) * w.doubleValueOf(i)
+ filter.getParameter()
* inertiaWeight.doubleValueOf(i)); // w.doubleValueOf(i));//;
}
}
}
/** {@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);
// }
}
