net.sourceforge.cilib.pso.iterationstrategies.ASynchronousIterationStrategy Maven / Gradle / Ivy

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

import net.sourceforge.cilib.algorithm.population.AbstractIterationStrategy;
import net.sourceforge.cilib.pso.PSO;
import net.sourceforge.cilib.pso.particle.Particle;
import fj.F;
import fj.F2;
import fj.Function;
import fj.P2;
import fj.data.List;

/**
 * Implementation of the asynchronous iteration strategy for PSO.
 */
public class ASynchronousIterationStrategy extends AbstractIterationStrategy {

    private static final long serialVersionUID = -3511991873784185698L;

    private F additionalStep = Function.identity();

    /**
     * {@inheritDoc}
     */
    @Override
    public ASynchronousIterationStrategy getClone() {
        return this;
    }

    /**
     * This is an ASynchronous strategy:
     *
     * 
     *   For all particles:
     *   
     *     Update the particle velocity
     *     Update the particle position
     *     Calculate the particle fitness
     *     For all particles in the current particle's neighbourhood
     *     
     *       Update the neighbourhood best
     *     
     *   
     * 
     *
     * @param algorithm the algorithm to which an iteration is to be applied.
     */
    @Override
    public void performIteration(final PSO algorithm) {
        final fj.data.List topology = algorithm.getTopology();

        algorithm.setTopology(topology.zipIndex().foldLeft(new F2, P2, fj.data.List>() {
			@Override
			public List f(List accum, P2 item) {
				item._1().updateVelocity();
	            item._1().updatePosition();

	            boundaryConstraint.enforce(item._1());
	            item._1().calculateFitness();

	            Particle newParticle = additionalStep.f(item._1());
                List result = accum.snoc(newParticle);
	            fj.data.List intermediate = result.append(topology.drop(item._2()+1));

	            for (Particle other : algorithm.getNeighbourhood().f(intermediate, newParticle)) {
	                if (newParticle.getSocialFitness().compareTo(other.getNeighbourhoodBest().getSocialFitness()) > 0) {
	                    other.setNeighbourhoodBest(newParticle);
	                }
	            }
	            return result;
			}
        }, List.nil()));
    }

    public void setAdditionalStep(F additionalStep) {
        this.additionalStep = additionalStep;
    }

    public F getAdditionalStep() {
        return additionalStep;
    }
}