/**
* Compare the {@linkplain Entity} objects returning the desired ordering.
*
* @param e1 The first {@linkplain Entity} to be used in the comparison.
* @param e2 The second {@linkplain Entity} to be used in the comparison.
* @return -1 if e1 is less than e2; 0 if e1 and e2 are equal 1 if e2 is greater than e1
*/
@Override
public int compare(E e1, E e2) {
Fitness f1 = e1.getFitness();
Fitness f2 = e2.getFitness();
return f1.compareTo(f2);
}
/**
* 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;
}
@Override
public Vector get(Particle particle) {
Vector newPos = (Vector) delegate.get(particle);
Particle tmp = particle.getClone();
tmp.setPosition(newPos);
Fitness newFitness = particle.getBehaviour().getFitnessCalculator().getFitness(tmp);
final UniformDistribution uniform = new UniformDistribution();
Vector newPBest =
newPos.plus(
Vector.newBuilder()
.repeat(newPos.size(), Real.valueOf(1.0))
.build()
.multiply(
new P1<Number>() {
@Override
public Number _1() {
return uniform.getRandomNumber(
-granularity.getParameter(), granularity.getParameter());
}
}));
tmp.setPosition(newPos);
Fitness newPBestFitness = particle.getBehaviour().getFitnessCalculator().getFitness(tmp);
if (newPBestFitness.compareTo(newFitness) < 0) {
Vector tmpVector = Vector.copyOf(newPos);
newPos = newPBest;
newPBest = tmpVector;
newPBestFitness = newFitness;
}
double dot =
((Vector) particle.getNeighbourhoodBest().getBestPosition())
.subtract(newPos)
.dot(newPBest.subtract(newPos));
if (dot < 0) {
return (Vector) particle.getPosition();
}
particle.put(Property.BEST_POSITION, newPBest);
particle.put(Property.BEST_FITNESS, newPBestFitness);
return newPos;
}