/**
* 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;
}
