遗传算法解决TSP问答(C)

遗传算法解决TSP问题（C++版）遗传算法流程：交叉，编译，计算适应度，保存最优个体。

其中交叉过程是选择最优的两个染色体进行交叉操作，本文采用的是轮盘赌算法。

#include<iostream>#include<cstdlib>#include<ctime>using namespace std;#define population 200//种群数量#define pc 0.9//交叉的概率#define pm 0.1//变异的概率#define count 200//迭代的次数#define num 10//城市的数量int** city;//存放每个个体的访问顺序int path[10][10] = {//0, 1, 2, 3, 4, 5, 6, 7, 8, 9{ 0, 23, 93, 18, 40, 34, 13, 75, 50, 35 },//0{ 23, 0, 75, 4, 72, 74, 36, 57, 36, 22 },//1{ 93, 75, 0, 64, 21, 73, 51, 25, 74, 89 },//2{ 18, 4, 64, 0, 55, 52, 8, 10, 67, 1 }, //3{ 40, 72, 21, 55, 0, 43, 64, 6, 99, 74 }, //4{ 34, 74, 73, 52, 43, 0, 43, 66, 52, 39 },//5{ 13, 36, 51, 8, 64, 43, 0, 16, 57, 94 },//6{ 75, 57, 25, 10, 6, 66, 16, 0, 23, 11 }, //7{ 50, 36, 74, 67, 99, 52, 57, 23, 0, 42 },//8{ 35, 22, 89, 1, 74, 39, 94, 11, 42, 0 }//9};int* dis;//存放每个个体的访问顺序下的路径长度double* fitness;//存放灭个个体的适应度int min_dis = 1000000;int min_index = -1;int* min_path;//初始化种群void init(){int *a = new int[num];for (int i = 0; i<num; i++){a[i] = i;}city = new int*[population];for (int i = 0; i<population; i++){city[i] = new int[num];}for (int i = 0; i<population; i++){for (int j = num - 1; j >= 0; j--){int n = rand() % (j + 1);//产出的数是0-j,保证交换的后面的数不会再被交换swap(a[j], a[n]);//保证a里面全是0-(num-1)的数，无重复的数，只是顺序颠倒city[i][j] = a[j];}}delete[]a;dis = new int[population];fitness = new double[population];min_path = new int[num];}//计算适应度void compute(){//cout << "do compute now. " << endl;double total = 0;for (int i = 0; i<population; i++){//计算每种情况下，路径的长度dis[i] = 0;int a = city[i][0], b;for (int j = 1; j<num; j++){b = city[i][j];dis[i] += path[a][b];a = b;}dis[i] += path[b][city[i][0]];fitness[i] = 1.0 / dis[i];//以距离的倒数作为适应度函数值total += fitness[i];}}//选择适应度高的物种，采用轮盘赌算法int select(){double total = 0;for (int i = 0; i<population; i++){total += fitness[i];}double size = rand() / (double)RAND_MAX * total;//保证不产生0//cout << "size " << size << endl;double sum = 0;int i = 0;while (sum <= size&&i<population){sum += fitness[++i];}return --i;//返回被选中的个体}int getMinDis(){int result = dis[0];int index = 0;for (int i = 1; i<population; i++){if (result > dis[i]){result = dis[i];index = i;}}return index;}int getMaxDis(){int result = dis[0];int index = 0;for (int i = 1; i<population; i++){if (result < dis[i]){result = dis[i];index = i;}}return index;}void save(){int current_min_index = getMinDis();int current_max_index = getMaxDis();if (dis[current_min_index] < min_dis){min_dis = dis[current_min_index];for (int i = 0; i < num; i++){min_path[i] =city[current_min_index][i];}//cout << "current min dis is: " << min_dis << endl;}else{for (int i = 0; i<num; i++){city[current_max_index][i] = min_path[i];}dis[current_max_index] = min_dis;fitness[current_max_index] = 1.0 / min_dis;}}//最优保存算法bool isExist(int value, int* array, int len){for (int i = 0; i<len; i++){if (value == array[i])return true;}return false;}void convert(int p1, int p2, int* src, int* dst){int len = p2 - p1 + 1;int* temp = new int[len];for (int i = p1; i <= p2; i++){temp[i - p1] = src[i];}int j = (p2 + 1) % num;for (int i = 1; i <= num; i++){int index = (i + p2) % num;if (!isExist(dst[index], temp, len)){dst[j] = dst[index];j = (j + 1) % num;}}for (int i = p1; i <= p2; i++){dst[i] = src[i];}delete[]temp;}//交叉，采用次序交叉算法void cross(){//cout << "do cross now. " << endl;for (int k = 0; k<population; k += 2){int a = select();int b = select();while (a == b){b = select();//保证被选中的个体不是一样的//cout << "same " << b << endl;}//cout << "choose popuilation" << a << " " << b << endl;double p = rand() / double(RAND_MAX);//cout << "cross rate is " << p << endl;int* a1 = new int[num];int* a2 = new int[num];int* b1 = new int[num];int* b2 = new int[num];for (int i = 0; i<num; i++){a1[i] = city[a][i];a2[i] = city[b][i];b1[i] = a2[i];b2[i] = a1[i];}if (p<pc)//满足交叉条件{//选择交叉的两点，并保证p1<p2int p1 = -1;int p2 = -1;while (p1 == p2){p1 = rand() % num;p2 = rand() % num;if (p1>p2){swap(p1, p2);}}//cout << "choose pos " << p1 << " " << p2 << endl;//开始交叉convert(p1, p2, a1, b1);convert(p1, p2, a2, b2);for (int i = 0; i<num; i++){city[k][i] = b1[i];city[k + 1][i] = b2[i];}}else{for (int i = 0; i<num; i++){city[k][i] = a1[i];city[k + 1][i] = a2[i];}}delete[]a1;delete[]a2;delete[]b1;delete[]b2;}}//变异,采用对换操作进行变异void morphis(){//cout << "do morphis now. " << endl;for (int i = 0; i<population; i++){double p = rand() / double(RAND_MAX);//cout << "morphis rate is " << p << endl;if (p<pm)//执行变异{int a = -1, b = -1;while (a == b){a = rand() % num;b = rand() % num;}swap(city[i][a], city[i][b]);}}}int getdis(){//compute();int result = dis[0];int index = 0;for (int i = 1; i<population; i++){if (result > dis[i]){result = dis[i];index = i;}}return result;}//释放申请的数组的空间void dispose(){for (int i = 0; i<population; i++){delete[]city[i];}delete[]city;delete[]dis;delete[]fitness;}int main(){init();//初始化种群int i = 0;srand(time(0));compute();while (i<count){cross();//交叉morphis();//变异compute();//计算适应度save();//保存当前最优的个体//cout << "count " << i++ << endl;cout << getdis() << " ";//输出结果//cout << min_index << " ";if (++i % 10 == 0)cout << endl;}compute();cout << "min distance is: " << min_dis << endl;for (int i = 0; i<num; i++)cout << min_path[i] << " ";cout << endl;dispose();//释放空间return 0;}。