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 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