遗传算法-JAVA【精品毕业设计】(完整版)

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<popSize-1) best ++;
while(citys[bad].isSelected != 0 && bad<popSize-1) 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;。