/*
* Reinitialises part of, or the whole, population
* @param topology The population to be reinitialised
*/
private void reinitialisePosition(fj.data.List<ClusterParticle> topology) {
int index = 0;
for (int i = index; i < topology.length(); i += reinitialisationInterval) {
ClusterParticle c = topology.index(i);
c.reinitialise();
c.calculateFitness();
}
}
/** 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()));
}
/*
* Performs an iteration of the standard co-operative algorithm.
* It holds a context particle, adapts the swarms to hold the context particle
* with the appropriate dimension difference,updates the personal and global
* bests and then updates the particles.
*/
@Override
public void performIteration(CooperativePSO algorithm) {
int populationIndex = 0;
table = new StandardDataTable();
DataClusteringPSO pso;
Topology newTopology;
ClusterParticle particleWithContext;
for (PopulationBasedAlgorithm currentAlgorithm : algorithm.getPopulations()) {
table =
((SinglePopulationDataClusteringIterationStrategy)
((DataClusteringPSO) currentAlgorithm).getIterationStrategy())
.getDataset();
if (!contextinitialised) {
initialiseContextParticle(algorithm);
}
pso = ((DataClusteringPSO) currentAlgorithm);
newTopology = ((DataClusteringPSO) currentAlgorithm).getTopology().getClone();
newTopology.clear();
for (ClusterParticle particle : ((DataClusteringPSO) currentAlgorithm).getTopology()) {
clearDataPatterns(contextParticle);
assignDataPatternsToParticle(
(CentroidHolder) contextParticle.getCandidateSolution(), table);
contextParticle.calculateFitness();
particleWithContext = new ClusterParticle();
particleWithContext.setCandidateSolution(contextParticle.getCandidateSolution().getClone());
particleWithContext
.getProperties()
.put(EntityType.Particle.BEST_POSITION, particle.getBestPosition().getClone());
particleWithContext
.getProperties()
.put(EntityType.Particle.BEST_FITNESS, particle.getBestFitness().getClone());
particleWithContext
.getProperties()
.put(EntityType.Particle.VELOCITY, particle.getVelocity().getClone());
particleWithContext.setNeighbourhoodBest(particle.getNeighbourhoodBest());
((CentroidHolder) particleWithContext.getCandidateSolution())
.set(
populationIndex,
((CentroidHolder) particle.getCandidateSolution()).get(populationIndex));
particleWithContext
.getProperties()
.put(
EntityType.Particle.Count.PBEST_STAGNATION_COUNTER,
particle
.getProperties()
.get(EntityType.Particle.Count.PBEST_STAGNATION_COUNTER)
.getClone());
particleWithContext.setCentroidInitialisationStrategy(
particle.getCentroidInitialisationStrategyCandidate().getClone());
clearDataPatterns(particleWithContext);
assignDataPatternsToParticle(
(CentroidHolder) particleWithContext.getCandidateSolution(), table);
particleWithContext.calculateFitness();
if (particleWithContext.getFitness().compareTo(particleWithContext.getBestFitness()) > 0) {
particle.getProperties().put(EntityType.Particle.BEST_POSITION, particle.getPosition());
particle.getProperties().put(EntityType.Particle.BEST_FITNESS, particle.getFitness());
particleWithContext
.getProperties()
.put(EntityType.Particle.BEST_POSITION, particle.getPosition());
particleWithContext
.getProperties()
.put(EntityType.Particle.BEST_FITNESS, particle.getFitness());
}
if (particleWithContext.getBestFitness().compareTo(contextParticle.getFitness()) > 0) {
((CentroidHolder) contextParticle.getCandidateSolution())
.set(
populationIndex,
((CentroidHolder) particle.getCandidateSolution()).get(populationIndex));
}
if (contextParticle.getFitness().compareTo(contextParticle.getBestFitness()) > 0) {
contextParticle
.getProperties()
.put(EntityType.Particle.BEST_POSITION, contextParticle.getPosition())
.getClone();
contextParticle
.getProperties()
.put(EntityType.Particle.BEST_FITNESS, contextParticle.getFitness())
.getClone();
}
newTopology.add(particleWithContext);
}
if (elitist) {
contextParticle
.getProperties()
.put(EntityType.CANDIDATE_SOLUTION, contextParticle.getBestPosition().getClone());
contextParticle
.getProperties()
.put(EntityType.FITNESS, contextParticle.getBestFitness().getClone());
}
pso.setTopology(newTopology);
pso.performIteration();
populationIndex++;
}
}