/** {@inheritDoc} */
@Override
public void randomise() {
double tmp =
Rand.nextDouble() * (getBounds().getUpperBound() - getBounds().getLowerBound())
+ getBounds().getLowerBound();
this.value = Double.valueOf(tmp).intValue();
}
@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();
}
/**
* After every {@link #interval} iteration, pick {@link #numberOfSentries a number of} random
* entities from the given {@link Algorithm algorithm's} topology and compare their previous
* fitness values with their current fitness values. An environment change is detected when the
* difference between the previous and current fitness values are >= the specified {@link
* #epsilon} value.
*
* @param algorithm used to get hold of topology of entities and number of iterations
* @return true if a change has been detected, false otherwise
*/
@Override
public <A extends HasTopology & Algorithm & HasNeighbourhood> boolean detect(A algorithm) {
if ((AbstractAlgorithm.get().getIterations() % interval == 0)
&& (AbstractAlgorithm.get().getIterations() != 0)) {
List all = Java.List_ArrayList().f(algorithm.getTopology());
for (int i = 0; i < numberOfSentries.getParameter(); i++) {
// select random sentry entity
int random = Rand.nextInt(all.size());
StandardParticle sentry = (StandardParticle) all.get(random);
// check for change
// double previousFitness = sentry.getFitness().getValue();
boolean detectedChange = false;
if (sentry.getFitness().getClass().getName().matches("MinimisationFitness")) {
Fitness previousFitness = sentry.getFitness();
sentry.calculateFitness();
Fitness currentFitness = sentry.getFitness();
if (Math.abs(previousFitness.getValue() - currentFitness.getValue()) >= epsilon) {
detectedChange = true;
}
} else if (sentry.getFitness().getClass().getName().matches("StandardMOFitness")) {
MOFitness previousFitness = (MOFitness) sentry.getFitness();
sentry.calculateFitness();
MOFitness currentFitness = (MOFitness) sentry.getFitness();
for (int k = 0; k < previousFitness.getDimension(); k++)
if (Math.abs(
previousFitness.getFitness(k).getValue()
- currentFitness.getFitness(k).getValue())
>= epsilon) {
detectedChange = true;
break;
}
}
if (detectedChange) {
System.out.println("Detected a change");
return true;
}
// remove the selected element from the all list preventing it from being selected again
all.remove(random);
}
}
return false;
}
/** {@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;
}
/** Test of reinitialiseContext method, of class CooperativeDataClusteringPSOIterationStrategy. */
@Test
public void testReinitialiseContext() {
Rand.setSeed(0);
DataClusteringPSO instance = new DataClusteringPSO();
SlidingWindow window = new SlidingWindow();
window.setSourceURL("library/src/test/resources/datasets/iris2.arff");
QuantisationErrorMinimisationProblem problem = new QuantisationErrorMinimisationProblem();
problem.setWindow(window);
problem.setDomain("R(-5.12:5.12)");
IterationStrategy strategy = new StandardDataClusteringIterationStrategy();
CentroidBoundaryConstraint constraint = new CentroidBoundaryConstraint();
constraint.setDelegate(new RandomBoundaryConstraint());
strategy.setBoundaryConstraint(constraint);
instance.setOptimisationProblem(problem);
DataDependantPopulationInitialisationStrategy init =
new DataDependantPopulationInitialisationStrategy();
init.setEntityType(new ClusterParticle());
init.setEntityNumber(2);
instance.setInitialisationStrategy(init);
instance.setOptimisationProblem(problem);
instance.addStoppingCondition(new MeasuredStoppingCondition());
CooperativePSO cooperative = new CooperativePSO();
cooperative.addStoppingCondition(
new MeasuredStoppingCondition(new Iterations(), new Maximum(), 30));
cooperative.addPopulationBasedAlgorithm(instance);
cooperative.setOptimisationProblem(problem);
cooperative.performInitialisation();
ClusterParticle particleBefore = instance.getTopology().head().getClone();
cooperative.run();
ClusterParticle particleAfter = instance.getTopology().head().getClone();
Assert.assertFalse(particleAfter.getPosition().containsAll(particleBefore.getPosition()));
}
