/**
* 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;
}
/**
* 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);
}
}
}
}
