psoalgorithm.java

用Java实现的粒子群优化算法

// Decompiled by Jad v1.5.7f. Copyright 2000 Pavel Kouznetsov.
// Jad home page: http://www.geocities.com/SiliconValley/Bridge/8617/jad.html
// Decompiler options: packimports(3) 
// Source File Name:   PSOAlgorithm.java

package ci.ec.pso.real;

import ci.real.RealIndividual;
import java.io.PrintStream;

// Referenced classes of package ci.ec.pso.real:
//            PSOBaseRealPopulation, PSOParameters, PSORealPopulation

public class PSOAlgorithm
{

    public PSOAlgorithm(int i, int j, PSOParameters psoparameters)
    {
        para = psoparameters;
        population = new PSORealPopulation(i, j, psoparameters);
        population.initialization(psoparameters.getLeftInitialRange(), psoparameters.getRightInitialRange());
        bestPopulation = new PSOBaseRealPopulation(population);
        velocity = new PSOBaseRealPopulation(i, j);
        velocity.initialization(psoparameters.getMaximumVelocity());
        bestFitness = new double[i];
        for(int k = 0; k < i; k++)
            bestFitness[k] = 1.7976931348623157E+308D;

        globalBestIndex = 0;
        globalBestFitness = 1.7976931348623157E+308D;
        inertiaWeight = psoparameters.getInitialWeight();
    }

    public static void main(String args[])
    {
        PSOParameters psoparameters = new PSOParameters(0.90000000000000002D, 20, 100D, 200D, 30D, 70D, 1000, "Sphere", 10);
        PSOAlgorithm psoalgorithm = new PSOAlgorithm(2, 10, psoparameters);
        System.out.println("initial PSO:\n" + psoalgorithm.toString());
        System.out.println("initial weight: " + psoalgorithm.inertiaWeight + "\n");
        psoalgorithm.oneIteration();
        psoalgorithm.oneIteration();
        System.out.println("initial weight after two iterations : " + psoalgorithm.inertiaWeight + "\n");
    }

    public void oneIteration()
    {
        int i = population.getPopulationSize();
        population.evaluate();
        setFitness();
        for(int j = 0; j < i; j++)
        {
            if(bestFitness[j] > currentFitness[j])
            {
                bestFitness[j] = currentFitness[j];
                bestPopulation.individualAt(j).copyof(population.individualAt(j));
            }
            if(currentFitness[j] < globalBestFitness)
            {
                globalBestIndex = j;
                globalBestFitness = currentFitness[j];
            }
        }

        setNewInertiaWeightLinearly();
        updateVelocity();
        adjustVelocity();
        updatePosition();
    }

    private void setNewInertiaWeightLinearly()
    {
        double d = (para.getInitialWeight() - 0.40000000000000002D) / (double)para.getMaximumGeneration();
        inertiaWeight = inertiaWeight - d;
    }

    private void updateVelocity()
    {
        velocity.multiply(inertiaWeight);
        PSOBaseRealPopulation psobaserealpopulation = new PSOBaseRealPopulation(velocity);
        psobaserealpopulation = updateVelocityStep();
        velocity.add(psobaserealpopulation);
    }

    private void adjustVelocity()
    {
        velocity.valueLimit(para.getMaximumVelocity());
    }

    private PSOBaseRealPopulation updateVelocityStep()
    {
        return population.getUpdateVelocityStep(bestPopulation, globalBestIndex);
    }

    private void updatePosition()
    {
        population.add(velocity);
    }

    public double[] getFitness()
    {
        return currentFitness;
    }

    public void setFitness()
    {
        currentFitness = population.getFitness();
    }

    public PSORealPopulation getPopulation()
    {
        return population;
    }

    public PSOBaseRealPopulation getBestPopulation()
    {
        return bestPopulation;
    }

    public PSOBaseRealPopulation getVelocity()
    {
        return velocity;
    }

    public double getGlobalBestFitness()
    {
        return globalBestFitness;
    }

    public String toString()
    {
        String s = "";
        s = s + "Population: \n" + population.toString();
        s = s + "best population: \n" + bestPopulation.toString();
        s = s + "velocity: \n" + velocity.toString();
        s = s + "global best index: " + globalBestIndex;
        s = s + "global best fitness: " + globalBestFitness + "\n";
        return s;
    }

    private PSORealPopulation population;
    private PSOBaseRealPopulation bestPopulation;
    private PSOBaseRealPopulation velocity;
    private PSOParameters para;
    private double inertiaWeight;
    private double bestFitness[];
    private double currentFitness[];
    private int globalBestIndex;
    private double globalBestFitness;
}