net.sourceforge.cilib.pso.iterationstrategies.moo.NonDominatedMOOSynchronousIterationStrategy Maven / Gradle / Ivy

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/**           __  __
 *    _____ _/ /_/ /_    Computational Intelligence Library (CIlib)
 *   / ___/ / / / __ \   (c) CIRG @ UP
 *  / /__/ / / / /_/ /   http://cilib.net
 *  \___/_/_/_/_.___/
 */
package net.sourceforge.cilib.pso.iterationstrategies.moo;

import fj.data.List;
import net.sourceforge.cilib.algorithm.AbstractAlgorithm;
import net.sourceforge.cilib.algorithm.population.AbstractIterationStrategy;
import net.sourceforge.cilib.pso.particle.Particle;
import net.sourceforge.cilib.problem.Problem;
import net.sourceforge.cilib.problem.solution.MOFitness;
import net.sourceforge.cilib.problem.solution.OptimisationSolution;
import net.sourceforge.cilib.pso.PSO;

/**
 * Implementation of the synchronous iteration strategy for PSO.
 *
 */
public class NonDominatedMOOSynchronousIterationStrategy extends AbstractIterationStrategy {
    /**
     * {@inheritDoc}
     */
    @Override
    public NonDominatedMOOSynchronousIterationStrategy getClone() {
        return this;
    }

    /**
     * This is an Synchronous strategy:
     *
     * 
     * For all particles:
     * Update the particle velocity
     *     Update the particle position
     * For all particles:
     * Calculate the particle fitness
     *     For all particles in the current particle's neighbourhood:
     *     Update the neighbourhood best
     * 
     *
     * @param pso The {@link PSO} to have an iteration applied.
     */
    @Override
    public void performIteration(PSO pso) {
        List 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 : pso.getNeighbourhood().f(topology, 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();

                if (fitness1.compareTo(fitness2) >= 0) {
                    other.setNeighbourhoodBest(current);
                }
            }
        }
    }

}