java源代码实例
遗传算法
<!-- Code highlighting produced by Actipro CodeHighlighter (freeware) http://www.CodeHighlighter.com/ --> 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(){ l分享到：
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