@Override
public OptimisationSolution getBestSolution() {
OptimisationSolution bestSolution = subPopulationsAlgorithms.get(0).getBestSolution();
for (PopulationBasedAlgorithm currentAlgorithm : subPopulationsAlgorithms) {
if (bestSolution.compareTo(currentAlgorithm.getBestSolution()) < 0) {
bestSolution = currentAlgorithm.getBestSolution();
}
}
return bestSolution;
}
/**
* Perform an algorithm iteration, then restart the {@linkplain Algorithm} and increment the
* number of restarts.
*/
@Override
public void algorithmIteration() {
algorithm.run();
singleIteration.reset();
OptimisationSolution tmp = optimisationAlgorithm.getBestSolution();
if (tmp.getFitness().compareTo(fitness) > 0) {
fitness = tmp.getFitness();
solution = tmp;
}
if (algorithm.isFinished()) {
problem.resetFitnessCounter();
algorithm.performInitialisation();
++restarts;
}
}
/**
* 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);
}
}
}
}
@Override
public double getPercentageCompleted(Algorithm algorithm) {
// check if this is the first iteration
if (previousBest == null) {
previousBest = algorithm.getBestSolution();
return 0.0;
}
// get the distance between previous and current best
double distance =
distMeasure.distance(
(Vector) previousBest.getPosition(),
(Vector) algorithm.getBestSolution().getPosition());
// compare to see change
if (distance < minChange) minChangeCounter++;
else minChangeCounter = 0;
return minChangeCounter / maxConsecutiveMinChange;
}
