优化算法——差分进化算法(DE)

package org.zzy.de;

import java.util.Random;

public class Population {
	public static int NP = 1000;// 种群规模
	public static int size = 10;// 个体的长度
	public static int xMin = -10;// 最小值
	public static int xMax = 10;// 最大值
	public static double F = 0.5;// 变异的控制参数
	public static double CR = 0.8;// 杂交的控制参数

	private double X[][] = new double[NP][size];// 个体
	private double XMutation[][] = new double[NP][size];
	private double XCrossOver[][] = new double[NP][size];
	private double fitness_X[] = new double[NP];// 适应值

	public double[][] getX() {
		return X;
	}

	/**
	 * 矩阵的复制
	 * 
	 * @param x把x复制给个体
	 */
	public void setX(double x[][]) {
		for (int i = 0; i < NP; i++) {
			for (int j = 0; j < size; j++) {
				this.X[i][j] = x[i][j];
			}
		}
	}

	public double[] getFitness_X() {
		return fitness_X;
	}

	public void setFitness_X(double[] fitness_X) {
		for (int i = 0; i < NP; i++) {
			this.fitness_X[i] = fitness_X[i];
		}
	}

	public double[][] getXMutation() {
		return XMutation;
	}

	public void setXMutation(double xMutation[][]) {
		for (int i = 0; i < NP; i++) {
			for (int j = 0; j < size; j++) {
				this.XMutation[i][j] = xMutation[i][j];
			}
		}
	}

	public double[][] getXCrossOver() {
		return XCrossOver;
	}

	public void setXCrossOver(double xCrossOver[][]) {
		for (int i = 0; i < NP; i++) {
			for (int j = 0; j < size; j++) {
				this.XCrossOver[i][j] = xCrossOver[i][j];
			}
		}
	}

	/**
	 * 适应值的计算
	 * 
	 * @param XTemp根据个体计算适应值
	 * @return返回适应值
	 */
	public double CalculateFitness(double XTemp[]) {
		double fitness = 0;
		for (int i = 0; i < size; i++) {
			fitness += XTemp[i] * XTemp[i];// 做一个X的平方相加的函数
		}
		return fitness;
	}

	/**
	 * 初始化：随机初始化种群，计算个体的适应值
	 */
	public void Initialize() {
		double XTemp[][] = new double[NP][size];
		double FitnessTemp[] = new double[NP];
		Random r = new Random();
		for (int i = 0; i < NP; i++) {
			for (int j = 0; j < size; j++) {
				XTemp[i][j] = xMin + r.nextDouble() * (xMax - xMin);
			}
			// 计算适应值
			FitnessTemp[i] = CalculateFitness(XTemp[i]);
		}

		this.setX(XTemp);
		this.setFitness_X(FitnessTemp);
	}

	/******** 变异操作 ***********/
	public void Mutation() {
		double XTemp[][] = new double[NP][size];
		double XMutationTemp[][] = new double[NP][size];
		XTemp = this.getX();
		Random r = new Random();
		for (int i = 0; i < NP; i++) {
			int r1 = 0, r2 = 0, r3 = 0;
			while (r1 == i || r2 == i || r3 == i || r1 == r2 || r1 == r3
					|| r2 == r3) {// 取r1,r2,r3
				r1 = r.nextInt(NP);
				r2 = r.nextInt(NP);
				r3 = r.nextInt(NP);
			}
			for (int j = 0; j < size; j++) {
				XMutationTemp[i][j] = XTemp[r1][j] + F
						* (XTemp[r2][j] - XTemp[r3][j]);
			}
		}
		this.setXMutation(XMutationTemp);
	}

	/**
	 * 交叉操作
	 */
	public void CrossOver() {
		double XTemp[][] = new double[NP][size];
		double XMutationTemp[][] = new double[NP][size];
		double XCrossOverTemp[][] = new double[NP][size];

		XTemp = this.getX();
		XMutationTemp = this.getXMutation();
		// 交叉操作
		Random r = new Random();
		for (int i = 0; i < NP; i++) {
			for (int j = 0; j < size; j++) {
				double rTemp = r.nextDouble();
				if (rTemp <= CR) {
					XCrossOverTemp[i][j] = XMutationTemp[i][j];
				} else {
					XCrossOverTemp[i][j] = XTemp[i][j];
				}
			}
		}
		this.setXCrossOver(XCrossOverTemp);
	}

	/**
	 * 选择操作：使用贪婪选择策略
	 */
	public void Selection() {
		double XTemp[][] = new double[NP][size];
		double XCrossOverTemp[][] = new double[NP][size];
		double FitnessTemp[] = new double[NP];
		double FitnessCrossOverTemp[] = new double[NP];
		
		XTemp = this.getX();
		XCrossOverTemp = this.getXCrossOver();// 交叉变异后的个体
		FitnessTemp = this.getFitness_X();
		
		// 对群体进行重新设置
		for (int i = 0; i < NP; i++) {
			FitnessCrossOverTemp[i] = CalculateFitness(XCrossOverTemp[i]);
			if (FitnessCrossOverTemp[i] < FitnessTemp[i]) {
				for (int j = 0; j < size; j++){
					XTemp[i][j] = XCrossOverTemp[i][j];
				}
				FitnessTemp[i] = FitnessCrossOverTemp[i];
			}
		}
		this.setX(XTemp);
		this.setFitness_X(FitnessTemp);
	}

	/**
	 * 保存每一代的全局最优值
	 */
	public void SaveBest() {
		double FitnessTemp[] = new double[NP];
		FitnessTemp = this.getFitness_X();
		int temp = 0;
		// 找出最小值
		for (int i = 1; i < NP; i++) {
			if (FitnessTemp[temp] > FitnessTemp[i]) {
				temp = i;
			}
		}
		System.out.println(FitnessTemp[temp]);
	}
}

package org.zzy.test;

import org.zzy.de.Population;

public class DETest {
	public static void main(String args[]) {
		int gen = 0;
		int maxCycle = 1000;
		Population p = new Population();
		p.Initialize();// 初始化
		while (gen <= maxCycle) {
			p.Mutation();
			p.CrossOver();
			p.Selection();
			gen++;
			p.SaveBest();
		}
	}

}