复制代码 代码如下:
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;
while(tempDimension> 0 ){
if(citys[y].city[tempi] == -1){
citys[y].city[tempi] = (int)diff[dimension - tempDimension];
tempDimension --;
}
tempi ++;
}
}
/**
* @param individual 个体
* @param dimension 维数
* @return 佳点集 (用于交叉函数的交叉点） 在executeCrossover()函数中使用
*/
private double[] gp(int individual, int dimension){
double[] temp = new double[dimension];
double[] temp1 = new double[dimension];
int p = 2 * dimension + 3;
while(!isSushu(p))
p++;
for( int i = 0; i< dimension; i++){
temp[i] = 2*Math.cos(2*Math.PI*(i+1)/p) * (individual+1);
temp[i] = temp[i] - (int)temp[i];
if( temp [i]< 0)
temp[i] = 1+temp[i];
}
for( int i = 0; i< dimension; i++)
temp1[i] = temp[i];
Arrays.sort(temp1);
//排序
for( int i = 0; i< dimension; i++)
for( int j = 0; j< dimension; j++)
if(temp[j]==temp1[i])
temp[j] = i;
return temp;
}
/**
* 变异
*/
public void mutate(){
double random;
int temp;
int temp1;
int temp2;
for( int i = 0 ; i< popSize; i++){
random = Math.random();
if(random<=pmultation){
temp1 = (int)(Math.random() * (cityNum));
temp2 = (int)(Math.random() * (cityNum));
temp = citys[i].city[temp1];
citys[i].city[temp1] = citys[i].city[temp2];
citys[i].city[temp2] = temp;
}
}
}
/**
* 打印当前代数的所有城市序列，以及其相关的参数
*/
public void print(){
/**
* 初始化各城市之间的距离
*/
private void initDistance(){
for (int i = 0; i < cityNum; i++) {
for (int j = 0; j < cityNum; j++){
distance[i][j] = Math.abs(i-j);
}
}
}
/**
* 计算所有城市序列的适应度
*/
private void CalFitness() {
for (int i = 0; i < popSize; i++) {
for (int j = 0; j < cityNum - 1; j++)
citys[i].fitness += distance[citys[i].city[j]][citys[i].city[j + 1]];
citys[i].fitness += distance[citys[i].city[0]][citys[i].city[cityNum - 1]];
}
}
/**
* 计算选择概率
*/
private void CalSelectP(){
long sum = 0;
for( int i = 0; i< popSize; i++)
sum += citys[i].fitness;
for( int i = 0; i< popSize; i++)
citys[i].selectP = (double)citys[i].fitness/sum;
}
/**
* 计算期望概率
*/
private void CalExceptP(){
for( int i = 0; i< popSize; i++)
citys[i].exceptp = (double)citys[i].selectP * popSize;
}
/**
* 计算该城市序列是否较优，较优则被选择，进入下一代
*/
private void CalIsSelected(){
int needSelecte = popSize;
for( int i = 0; i< popSize; i++)
if( citys[i].exceptp<1){
citys[i].isSelected++;
needSelecte --;
}
double[] temp = new double[popSize];
for (int i = 0; i < popSize; i++) {
// temp[i] = citys[i].exceptp - (int) citys[i].exceptp;
// temp[i] *= 10;
temp[i] = citys[i].exceptp*10;
}
int j = 0;
while (needSelecte != 0) {
for (int i = 0; i < popSize; i++) {
if ((int) temp[i] == j) {
citys[i].isSelected++;
needSelecte--;
if (needSelecte == 0)
break;
}
}
j++;
}
}
/**
* @param x
* @return 判断一个数是否是素数的函数
*/
private boolean isSushu( int x){
if(x<2) return false;
for(int i=2;i<=x/2;i++)
if(x%i==0&&x!=2) return false;
return true;
}
/**
* @param x 数组
* @return x数组的值是否全部相等，相等则表示x.length代的最优结果相同，则算法结束
*/
private boolean isSame(long[] x){
for( int i = 0; i< x.length -1; i++)
if(x[i] !=x[i+1])
return false;
return true;
}
/**
* 打印任意代最优的路径序列
*/
private void printBestRoute(){
CalAll();
long temp = citys[0].fitness;
int index = 0;
for (int i = 1; i < popSize; i++) {
if(citys[i].fitness<temp){
temp = citys[i].fitness;
index = i;
}
}
System.out.println();
System.out.println("最佳路径的序列：");
for (int j = 0; j < cityNum; j++)
{
String cityEnd[]={cityName[citys[index].city[j]]};
for(int m=0;m<cityEnd.length;m++)
{
System.out.print(cityEnd[m] + " ");
}
}
//System.out.print(citys[index].city[j] + cityName[citys[index].city[j]] + " ");
//System.out.print(cityName[citys[index].city[j]]);
System.out.println();
}
/**
* 算法执行
*/
public void run(){
long[] result = new long[range];
//result初始化为所有的数字都不相等
for( int i = 0; i< range; i++)
result[i] = i;
int index = 0; //数组中的位置
int num = 1; //第num代
while(maxgens>0){
System.out.println("----------------- 第 "+num+" 代 -------------------------");
CalAll();
print();
pad();
crossover();
mutate();
maxgens --;
long temp = citys[0].fitness;
for ( int i = 1; i< popSize; i++)
if(citys[i].fitness<temp){
temp = citys[i].fitness;
}
System.out.println("最优的解："+temp);
result[index] = temp;
if(isSame(result))
break;
index++;
if(index==range)
index = 0;
num++;
}
printBestRoute();
}
/**
* @param a 开始时间
* @param b 结束时间
*/
public void CalTime(Calendar a,Calendar b){
long x = b.getTimeInMillis() - a.getTimeInMillis();
long y = x/1000;
x = x - 1000*y;
System.out.println("算法执行时间："+y+"."+x+" 秒");
}
/**
* 程序入口
*/
public static void main(String[] args) {
Calendar a = Calendar.getInstance(); //开始时间
Tsp tsp = new Tsp();
tsp.run();
Calendar b = Calendar.getInstance(); //结束时间
tsp.CalTime(a, b);
}
}
【java实现遗传算法实例分享(打印城市信息)】相关文章：