粒子群优化算法(PSO)

package psoo;

public class PSO {
	Particle particles[];
	Particle globalBestParticle;
	Function function;

	/**
	 * 构造粒子群
	 * 
	 * @param size粒子的个数
	 */
	public PSO(int size, int dim, Function function) {
		particles = new Particle[size];
		globalBestParticle = new Particle(dim, function);
		for (int i = 0; i < particles.length; i++) {
			particles[i] = new Particle(dim, function);
			particles[i].evaluate();
			if (globalBestParticle.fitness < particles[i].fitness) {
				// 找到更好的粒子
				globalBestParticle.copyFrom(particles[i]);

			}
		}
	}

	/**
	 * 粒子群算法的运算
	 * 
	 * @param maxiter运行最大代数
	 */
	public void run(int maxiter) {
		int iter = 0;
		while (iter < maxiter) {
			for (int i = 0; i < particles.length; i++) {
				particles[i].move(globalBestParticle);
			}
			for (int i = 0; i < particles.length; i++) {
				particles[i].evaluate();
				if (globalBestParticle.fitness < particles[i].fitness) {
					// 找到更好的粒子
					globalBestParticle.copyFrom(particles[i]);
					System.out.println("算法运行到第" + iter + "代,发现更好的解:" + globalBestParticle.fitness);
				}

			}
			iter++;
		}
		System.out.println("算法运行完毕,找到最优解:" + globalBestParticle.fitness);
	}
}

package psoo;

import java.util.Random;

public class Particle {
	public double[] x;// 粒子的位置
	double[] v;// 粒子的速度
	public double fitness;// 粒子当前求得的适应值
	double pfitnesss;// 粒子的历史最优解
	double[] px;// 粒子的历史最优位置
	Function function;

	static double w = 0.8;// 权重
	static double c1 = 2.0;
	static double c2 = 2.0;// 学习常数
	static Random random = new Random();// 随机数产生器
	static double lower = -100;
	static double upper = 100;// 求解问题的上下界

	int dim;// 维数

	/**
	 * 初始化粒子,随机放置粒子位置和产生一个随机速度
	 * 
	 */
	public Particle(int dim, Function fun) {
		this.function = fun;
		x = new double[dim];
		v = new double[dim];
		px = new double[dim];
		for (int i = 0; i < dim; i++) {
			x[i] = random.nextDouble() * (upper - lower) + lower;// 随机位置
			v[i] = random.nextDouble() * (upper - lower) + lower;// 随机速度
			px[i] = x[i];
		}
		pfitnesss = -1e8;
	}

	/**
	 * 评估粒子的适应值,也就是算出粒子代表的变量所对应的函数值 此处:f(x1,x2)=x1^2+x2^2;
	 */
	public double evaluate() {
		/**
		 * double sum = 0; for (double i : x) { sum += i * i; } fitness = sum;
		 **/
		fitness = function.evaluate(x);
		// 判断现在的适应值是不是比自己历史最优的还要好
		// 如果是,替换掉
		if (fitness > pfitnesss) {
			for (int i = 0; i < px.length; i++) {
				px[i] = x[i];
			}
			pfitnesss = fitness;
		}
		return fitness;
	}

	/**
	 * 粒子做一次移动,他的移动要根据自身的最优位置和全局的最优位置来决定
	 * 
	 * @param gbestParticle:全局最优位置
	 */
	public void move(Particle gbestParticle) {
		// 更新自己的位置
		for (int i = 0; i < x.length; i++) {
			x[i] = x[i] + v[i];
			// 判断不能越界
			if (x[i] > upper) {
				x[i] = upper;
			}
			if (x[i] < lower) {
				x[i] = lower;
			}
		}
		// 更新速度
		for (int i = 0; i < v.length; i++) {
			v[i] = w * v[i] + c1 * random.nextDouble() * (px[i] - v[i])
					+ c2 * random.nextDouble() * (gbestParticle.x[i] - x[i]);
			// 判断不能越界
			if (v[i] > (upper - lower)) {
				v[i] = upper - lower;
			}
			if (v[i] < (lower - upper)) {
				v[i] = lower - upper;
			}
		}
	}

	public void copyFrom(Particle particle) {
		this.fitness = particle.fitness;
		for (int i = 0; i < x.length; i++) {
			this.x[i] = particle.x[i];
		}

	}

	/**
	 * 打印染色体信息
	 */
	public String toString() {
		double result1 = 0;
		double result2 = 0;
		for (double i : x) {
			result1 += i;
		}
		for (double i : v) {
			result1 += i;
		}
		return "他的位置为:" + result1 + "他的方向为:" + result2 + "他的适应值为:" + fitness;
	}

}

注:实现接口处写需要求得函数即可

package psoo;

public interface Function {
	double evaluate(double values[]);// 一系列的输入,一系列的输出

}

package functions;

import psoo.Function;

public class Function2 implements Function {

	@Override
	public double evaluate(double[] values) {
		double sum = 0;
		double result = 0;
		for (int i = 0; i < values.length; i++) {
			sum += values[i] * values[i];
			// System.out.println(values[i]);
		}
		result = 10 - (Math.pow(Math.sin(Math.pow(sum, 1.0 / 2)), 2) - 0.5) / ((1 + 0.001 * sum) / (1 + 0.001 * sum))
				+ 0.5;
		return result;
	}

}

package main;

import functions.Function2;
import psoo.Function;
import psoo.PSO;

public class Main {

	public static void main(String[] args) {

		Function function2 = new Function2();
		PSO psoo = new PSO(10, 10, function2);
		psoo.run(10000);
	}

}