遗传算法
来源：互联网 发布：rfid读卡器软件 编辑：程序博客网 时间：2024/05/17 01:13
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); }}
