说明：算法为了演示功能，所以没有优化，没有异常处理等，仅作演示用。

/* * To change this template, choose Tools | Templates * and open the template in the editor. */ import java.util.Random; /** *粒子类 * 求解函数 f(x)=x1^2+(x2-x3)^2 的最大值 * @author FashionXu */ public class Particle { public double[] pos;//粒子的位置，求解问题多少维，则此数组为多少维 public double[] v;//粒子的速度，维数同位置 public double fitness;//粒子的适应度 public double[] pbest;//粒子的历史最好位置 public static double[] gbest;//所有粒子找到的最好位置 public static Random rnd; public static int dims; public static double w; public static double c1; public static double c2; double pbest_fitness;//历史最优解 /** * 返回low—uper之间的数 * @param low 下限 * @param uper 上限 * @return 返回low—uper之间的数 */ double rand(double low, double uper) { rnd = new Random(); return rnd.nextDouble() * (uper – low) + low; } /** * 初始化粒子 * @param dim 表示粒子的维数 */ public void initial(int dim) { pos = new double[dim]; v = new double[dim]; pbest = new double[dim]; fitness = -1e6; pbest_fitness = -1e6; dims = dim; for (int i = 0; i < dim; ++i) { pos[i] = rand(-10, 10); pbest[i] = pos[i]; v[i] = rand(-20, 20); } } /** * 评估函数值,同时记录历史最优位置 */ public void evaluate() { fitness = pos[0] * pos[0] + (pos[1] – pos[2]) * (pos[1] – pos[2]); if (fitness > pbest_fitness) { for (int i = 0; i < dims; ++i) { pbest[i] = pos[i]; } } } /** * 更新速度和位置 */ public void updatev() { for (int i = 0; i < dims; ++i) { v[i] = w * v[i] + c1 * rnd.nextDouble() * (pbest[i] – pos[i]) + c2 * rnd.nextDouble() * (gbest[i] – pos[i]); if (v[i] > 20) { v[i] = 20; } if (v[i] < -20) { v[i] = -20; } pos[i] = pos[i] + v[i]; if (pos[i] > 10) { pos[i] = 10; } if (pos[i] < -10) { pos[i] = -10; } } } }

/* * To change this template, choose Tools | Templates * and open the template in the editor. */ /** *粒子群类 * @author FashionXu */ public class PSO { /** * 粒子群 */ Particle[] pars; double global_best;//全局最优解 int pcount;//粒子的数量 /** * 粒子群初始化 * @param n 粒子的数量 */ public void init(int n) { pcount = n; global_best = -1e6; int index = -1; pars = new Particle[pcount]; //类的静态成员的初始化 Particle.c1 = 2; Particle.c2 = 2; Particle.w = 0.8; Particle.dims = 3; for (int i = 0; i < pcount; ++i) { pars[i] = new Particle(); pars[i].initial(3); pars[i].evaluate(); if (global_best < pars[i].fitness) { global_best = pars[i].fitness; index = i; } } Particle.gbest = new double[Particle.dims]; for (int i = 0; i < 3; ++i) { Particle.gbest[i] = pars[index].pos[i]; } } /** * 粒子群的运行 */ public void run() { int runtimes = 500; int index; while (runtimes > 0) { index = -1; //每个粒子更新位置和适应值 for (int i = 0; i < pcount; ++i) { pars[i].updatev(); pars[i].evaluate(); if (global_best < pars[i].fitness) { global_best = pars[i].fitness; index = i; } } //发现更好的解 if (index != -1) { for (int i = 0; i < 3; ++i) { Particle.gbest[i] = pars[index].pos[i]; } } –runtimes; } } /** * 显示程序求解结果 */ public void showresult() { System.out.println(“程序求得的最优解是” + global_best); System.out.println(“每一维的值是”); for (int i = 0; i < Particle.dims; ++i) { System.out.println(Particle.gbest[i]); } } }

/* * To change this template, choose Tools | Templates * and open the template in the editor. */ /** *测试粒子群算法 * @author FashionXu */ public class TestPSO { public static void main(String []arg) { PSO pso=new PSO(); pso.init(50); pso.run(); pso.showresult(); } }