粒子群算法的Java实现源码

package com.bjfu.pso; import java.util.ArrayList; import java.util.List; import java.util.Random; //import java.util.Vector; /** * this is the heart of the PSO program * the code is for 2-dimensional space problem * but you can easily modify it to solve higher dimensional space problem * @author xulei * */ public class PSOProcess implements PSOConstants { //private Vector<Particle> swarm = new Vector<Particle>(); private List<Particle> swarm = new ArrayList<Particle>(); //private Vector<Location> pBestLocation = new Vector<Location>(); private List<Location> pBestLocation = new ArrayList<Location>(); private double[] pBest = new double[SWARM_SIZE]; private double gBest; private Location gBestLocation; private double[] fitnessValueList = new double[SWARM_SIZE]; //方法调用返回下一个从这个随机数生成器的序列中均匀分布的0.0和1.0之间的double值。 Random generator = new Random(); public void execute() { //初始化种群 initializeSwarm(); //更新适应值列表 updateFitnessList(); //初始化pBest和pBestLocation for (int i = 0; i < SWARM_SIZE; i++) { pBest[i] = fitnessValueList[i]; pBestLocation.add(swarm.get(i).getLocation()); } int t = 0; double w; double err = 9999; while (t < MAX_ITERATION && err > ProblemSet.ERR_TOLERANCE) { //1、update pBest for (int i = 0; i < SWARM_SIZE; i++) { if (fitnessValueList[i] < pBest[i]) { pBest[i] = fitnessValueList[i]; pBestLocation.set(i, swarm.get(i).getLocation()); } } //2、update gBest int bestParticleIndex = PSOUtility.getMinPos(fitnessValueList); if (t == 0 || fitnessValueList[bestParticleIndex] < gBest) { gBest = fitnessValueList[bestParticleIndex]; gBestLocation = swarm.get(bestParticleIndex).getLocation(); } //计算惯性因子 w = W_UPPERBOUND - (((double) t) / MAX_ITERATION) * (W_UPPERBOUND - W_LOWERBOUND); for (int i = 0; i < SWARM_SIZE; i++) { double r1 = generator.nextDouble(); double r2 = generator.nextDouble(); Particle particle = swarm.get(i); //3、update velocity double[] newVel = new double[PROBLEM_DIMENSION]; newVel[0] = (w * particle.getVelocity().getVel()[0]) + (r1 * C1) * (pBestLocation.get(i).getLoc()[0] - particle.getLocation().getLoc()[0]) + (r2 * C2) * (gBestLocation.getLoc()[0] - particle.getLocation().getLoc()[0]); newVel[1] = (w * particle.getVelocity().getVel()[1]) + (r1 * C1) * (pBestLocation.get(i).getLoc()[1] - particle.getLocation().getLoc()[1]) + (r2 * C2) * (gBestLocation.getLoc()[1] - particle.getLocation().getLoc()[1]); Velocity velocity = new Velocity(newVel); particle.setVelocity(velocity); //4、update location double[] newLoc = new double[PROBLEM_DIMENSION]; newLoc[0] = particle.getLocation().getLoc()[0] + newVel[0]; newLoc[1] = particle.getLocation().getLoc()[1] + newVel[1]; Location location = new Location(newLoc); particle.setLocation(location); System.out.println("fasdfsadfsdafs"); } // minimizing the functions means it's getting closer to 0 err = ProblemSet.evaluate(gBestLocation) - 0; System.out.println("ITERATION " + t + ":"); System.out.println(" Best X: " + gBestLocation.getLoc()[0]); System.out.println(" Best Y: " + gBestLocation.getLoc()[1]); System.out.println(" Value: " + ProblemSet.evaluate(gBestLocation)); t++; updateFitnessList(); } //输出最终结果解： System.out.println("Solution found at iteration " + (t - 1) + ", the solution is : "); System.out.println(" Best X: " + gBestLocation.getLoc()[0]); System.out.println(" Best Y: " + gBestLocation.getLoc()[1]); } /** * 初始化种群:就是初始化数据 * 【后期从excle中读取】 */ public void initializeSwarm() { Particle particle; for (int i = 0; i < SWARM_SIZE; i++) { particle = new Particle(); //randomize location inside a space defined in Problem Set double[] loc = new double[PROBLEM_DIMENSION]; loc[0] = ProblemSet.LOC_X_LOW + generator.nextDouble() * (ProblemSet.LOC_X_HIGH - ProblemSet.LOC_X_LOW); loc[1] = ProblemSet.LOC_Y_LOW + generator.nextDouble() * (ProblemSet.LOC_Y_HIGH - ProblemSet.LOC_Y_LOW); Location location = new Location(loc); //randomize velocity in the range defined in Problem Set double[] vel = new double[PROBLEM_DIMENSION]; vel[0] = ProblemSet.VEL_LOW + generator.nextDouble() * (ProblemSet.VEL_HIGH - ProblemSet.VEL_LOW); vel[1] = ProblemSet.VEL_LOW + generator.nextDouble() * (ProblemSet.VEL_HIGH - ProblemSet.VEL_LOW); Velocity velocity = new Velocity(vel); particle.setLocation(location); particle.setVelocity(velocity); swarm.add(particle); } } /** * 更新适应值列表 */ public void updateFitnessList() { for (int i = 0; i < SWARM_SIZE; i++) { fitnessValueList[i] = swarm.get(i).getFitnessValue(); } } }