/** 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()));
}
@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());
}
}
/**
* 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);
}
@Override
public void algorithmIteration() {
multiSwarmsIterationStrategy.performIteration(this);
}
