遗传算法-JAVA

java常用代码---遗传算法

标签：javadistancecalendarstringrandom算法

2011-11-17 18:23 5815人阅读评论(1) 收藏举报分类：

数据结构与算法实践（23）

import java.util.*;

public class Tsp {

private String cityName[]={"北京","上海","天津","重庆","哈尔滨","长春","沈阳","呼和浩特","石家庄","太原","济南","郑州","西安","兰州","银川","西宁","乌鲁木齐","合肥","南京","杭州","长沙","南昌","武汉","成都","贵州","福建","台北","广州","海口","南宁","昆明","拉萨","香港","澳门"}; //private String cityEnd[]=new String[34];

private int cityNum=cityName.length; //城市个数

private int popSize = 50; //种群数量

private int maxgens = 20000; //迭代次数

private double pxover = 0.8; //交叉概率

private double pmultation = 0.05; //变异概率

private long[][] distance = new long[cityNum][cityNum];

private int range = 2000; //用于判断何时停止的数组区间

private class genotype {

int city[] = new int[cityNum]; //单个基因的城市序列

long fitness; //该基因的适应度

double selectP; //选择概率

double exceptp; //期望概率

int isSelected; //是否被选择

}

private genotype[] citys = new genotype[popSize];

/**

* 构造函数，初始化种群

*/

public Tsp() {

for (int i = 0; i < popSize; i++) {

citys[i] = new genotype();

int[] num = new int[cityNum];

for (int j = 0; j < cityNum; j++)

num[j] = j;

int temp = cityNum;

for (int j = 0; j < cityNum; j++) {

int r = (int) (Math.random() * temp);

citys[i].city[j] = num[r];

num[r] = num[temp - 1];

temp--;

}

citys[i].fitness = 0;

citys[i].selectP = 0;

citys[i].exceptp = 0;

citys[i].isSelected = 0;

}

initDistance();

}

/**

* 计算每个种群每个基因个体的适应度，选择概率，期望概率，和是否被选择。

*/

public void CalAll(){

for( int i = 0; i< popSize; i++){

citys[i].fitness = 0;

citys[i].selectP = 0;

citys[i].exceptp = 0;

citys[i].isSelected = 0;

}

CalFitness();

CalSelectP();

CalExceptP();

CalIsSelected();

}

/**

* 填充，将多选的填充到未选的个体当中

*/

public void pad(){

int best = 0;

int bad = 0;

while(true){

while(citys[best].isSelected <= 1 && best

while(citys[bad].isSelected != 0 && bad

for(int i = 0; i< cityNum; i++)

citys[bad].city[i] = citys[best].city[i];

citys[best].isSelected --;

citys[bad].isSelected ++;

bad ++;

if(best == popSize ||bad == popSize)

break;

}

}

/**

* 交叉主体函数

*/

public void crossover() {

int x;

int y;

int pop = (int)(popSize* pxover /2);

while(pop>0){

x = (int)(Math.random()*popSize);

y = (int)(Math.random()*popSize);

executeCrossover(x,y);//x y 两个体执行交叉

pop--;

}

}

/**

* 执行交叉函数

* @param 个体x

* @param 个体y

* 对个体x和个体y执行佳点集的交叉，从而产生下一代城市序列

*/

private void executeCrossover(int x,int y){

int dimension = 0;

for( int i = 0 ;i < cityNum; i++)

if(citys[x].city[i] != citys[y].city[i]){

dimension ++;

}

int diffItem = 0;

double[] diff = new double[dimension];

for( int i = 0 ;i < cityNum; i++){

if(citys[x].city[i] != citys[y].city[i]){

diff[diffItem] = citys[x].city[i];

citys[x].city[i] = -1;

citys[y].city[i] = -1;

diffItem ++;

}

}

Arrays.sort(diff);

double[] temp = new double[dimension];

temp = gp(x, dimension);

for( int k = 0; k< dimension;k++)

for( int j = 0; j< dimension; j++)

if(temp[j] == k){

double item = temp[k];

temp[k] = temp[j];

temp[j] = item;

item = diff[k];

diff[k] = diff[j];

diff[j] = item;

}

int tempDimension = dimension;

int tempi = 0;

while(tempDimension> 0 ){

if(citys[x].city[tempi] == -1){

citys[x].city[tempi] = (int)diff[dimension - tempDimension]; tempDimension --;

}

tempi ++;

}

Arrays.sort(diff);

temp = gp(y, dimension);

for( int k = 0; k< dimension;k++)

for( int j = 0; j< dimension; j++)

if(temp[j] == k){

double item = temp[k];

temp[k] = temp[j];

temp[j] = item;

item = diff[k];

diff[k] = diff[j];

diff[j] = item;

}

tempDimension = dimension;

tempi = 0;