@Override
public Vector get(Particle particle) {
Vector localGuide = (Vector) particle.getLocalGuide(); // personal best (yi)
Vector globalGuide = (Vector) particle.getGlobalGuide(); // global best (y^i)
Vector.Builder builder = Vector.newBuilder();
for (int i = 0; i < particle.getDimension(); ++i) {
// double tmp1 = cognitive.getParameter();
// double tmp2 = social.getParameter();
double sigma = Math.abs(localGuide.doubleValueOf(i) - globalGuide.doubleValueOf(i));
if (sigma == 0) {
sigma = 1;
}
// System.out.println("Sigma: "+sigma);
// 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);
if (Rand.nextDouble() < p) {
builder.add(
localGuide.doubleValueOf(i) + this.cauchyDistribution.getRandomNumber(mean, sigma));
} else {
builder.add(
globalGuide.doubleValueOf(i) + this.gaussianDistribution.getRandomNumber(mean, sigma));
}
}
return builder.build();
}
@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();
}
/**
* Update personal best if and only if the particle is within the bounds of the search space /
* problem.
*
* @param particle The particle to update.
*/
@Override
public void updatePersonalBest(Particle particle) {
if (!Types.isInsideBounds(particle.getPosition())) {
particle.getProperties().put(EntityType.FITNESS, InferiorFitness.instance());
return;
}
delegate.updatePersonalBest(particle);
}
@Test
public void testGetCandidateSolutions() {
Vector v1 = Vector.of(1.0, 2.0, 3.0);
Vector v2 = Vector.of(4.0, 5.0, 6.0);
Particle p1 = new StandardParticle();
Particle p2 = new StandardParticle();
p1.setCandidateSolution(v1);
p2.setCandidateSolution(v2);
List<Vector> list = Entities.<Vector>getCandidateSolutions(Arrays.asList(p1, p2));
assertEquals(v1, list.get(0));
assertEquals(v2, list.get(1));
}
@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 int compare(E o1, E o2) {
SinglePopulationBasedAlgorithm populationBasedAlgorithm =
(SinglePopulationBasedAlgorithm) AbstractAlgorithm.getAlgorithmList().index(0);
MOOptimisationProblem problem =
((MOOptimisationProblem) populationBasedAlgorithm.getOptimisationProblem());
Particle p1 = (Particle) o1;
Particle p2 = (Particle) o2;
MOFitness fitness1 = ((MOFitness) problem.getFitness(p1.getBestPosition()));
MOFitness fitness2 = ((MOFitness) problem.getFitness(p2.getBestPosition()));
int value = fitness1.compareTo(fitness2);
if (fitness1.compareTo(fitness2) == 0) {
int random = Rand.nextInt(20);
if (random > 10) value *= -1;
}
return value;
}
/** {@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());
}
/**
* 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);
}
}
}
}
