遗传算法求解TSP（C++）
1、输入数据文件：bayg29.tsp
29个城市的编号和坐标数据如下所示；
1 1150.0 1760.0
2 630.0 1660.0
3 40.0 2090.0
4 750.0 1100.0
5 750.0 2030.0
6 1030.0 2070.0
7 1650.0 650.0
8 1490.0 1630.0
9 790.0 2260.0
10 710.0 1310.0
11 840.0 550.0
12 1170.0 2300.0
13 970.0 1340.0
14 510.0 700.0
15 750.0 900.0
16 1280.0 1200.0
17 230.0 590.0
18 460.0 860.0
19 1040.0 950.0
20 590.0 1390.0
21 830.0 1770.0
22 490.0 500.0
23 1840.0 1240.0
24 1260.0 1500.0
25 1280.0 790.0
26 490.0 2130.0
27 1460.0 1420.0
28 1260.0 1910.0
29 360.0 1980.0
2 、头文件
#include <iostream>
#include <fstream>
#include <string>
#include <time.h>
#include <random>
using namespace std;
3、所需的类
代码中我们用到了四种类：City、Graph、Ranseti、GA
3.1 城市类City
class City
{
public:
string name;//城市名称
double x, y;//城市点的二维坐标
void shuchu()
{
std::cout <<name+":"<<"("<< x << "," << y <<")"<< endl;
}
};
3.2 包含城市的地图类Graph：
class Graph
{
public:
City city[citycount];//城市数组
double distance[citycount][citycount];//城市间的距离矩阵
void Readcoordinatetxt(string txtfilename)//读取城市坐标文件的函数
{
ifstream myfile(txtfilename, ios::in);
double x = 0, y = 0;
int z = 0;
if (!myfile.fail())
{
int i = 0;
while (!myfile.eof() && (myfile >> z >> x >> y))
{
city[i].name = to_string(_Longlong(z));//城市名称转化为字符串
city[i].x = x; city[i].y = y;
i++;
}
}
else
cout << "文件不存在";
myfile.close();//计算城市距离矩阵
for (int i = 0; i < citycount; i++)
for (int j = 0; j < citycount; j++)
{
distance[i][j] = sqrt((pow((city[i].x - city[j].x), 2) + pow((city[i].y - city[j].y), 2))/10.0);//计算城市ij之间的伪欧式距离
if (round(distance[i][j] < distance[i][j]))distance[i][j] = round(distance[i][j]) + 1;
else distance[i][j] = round(distance[i][j]);
}
}
void shuchu()
{
cout << "城市名称 " << "坐标x" << " " << "坐标y" << endl;
for (int i = 0; i < citycount; i++)
city[i].shuchu();
cout << "距离矩阵： " << endl;
for (int i = 0; i < citycount; i++)
{
for (int j = 0; j < citycount; j++)
{
if (j == citycount - 1)
std::cout << distance[i][j] << endl;
else
std::cout << distance[i][j] << " ";
}
}
}
};
3.3 染色体类Ranseti：
class Ranseti
{
public:
int *X;
double fitness;
void Init()
{
X = new int[citycount];
int *M = Random_N(citycount);
for (int j = 0; j < citycount; j++)
X[j] = *(M + j);
fitness=0;
}
void shuchu()
{
for (int j = 0; j < citycount; j++)
{
if (j == citycount -1) std::cout << X[j] << " "<<fitness<<endl;
else std::cout << X[j] << "->";
}
}
};
3.4 遗传算法类GA
class GA
{
public:
Ranseti *oldranseti;//上一代种群
Ranseti *ranseti;//下一代种群
int Pop_Size;//种群大小
int Itetime;//迭代次数
double Cross_Prob;//交叉概率
double Bianyi_Prob;//变异概率
Ranseti Bestranseti;//最佳染色体个体
double Bestlength;//最短路径的长度
double *Leiji_Prob;//累积选择概率
void Init(int popsize,int itetime,double crossprob,double bianyiprob,string filename)
{
Map_City.Readcoordinatetxt(filename);
Map_City.shuchu();
Pop_Size = popsize;
Itetime = itetime;
Cross_Prob = crossprob;
Bianyi_Prob = bianyiprob;
oldranseti = new Ranseti[Pop_Size];
ranseti = new Ranseti[Pop_Size];
Leiji_Prob = new double[Pop_Size];
for (int i = 0; i < Pop_Size; i++)
{
oldranseti[i].Init();
oldranseti[i].fitness = Evaluate(i);//初始化适应度
}
int bestid = -1;
for (int i = 0; i < Pop_Size; i++)
{
int j = 0;
for (; j < Pop_Size; j++)
{
if (oldranseti[i].fitness > oldranseti[j].fitness)break;
}
if (j == Pop_Size)
{
bestid = i;
break;
}
}
Bestranseti.Init();
Bestranseti.fitness = 0;
for (int j = 0; j < citycount; j++)
Bestranseti.X[j] = oldranseti[bestid].X[j];
for (int j = 0; j < citycount - 1; j++)
Bestranseti.fitness += Map_City.distance[Bestranseti.X[j]][Bestranseti.X[j + 1]];
Bestranseti.fitness += Map_City.distance[Bestranseti.X[citycount - 1]][Bestranseti.X[0]];
Bestlength = Bestranseti.fitness;
}
void shuchu()
{
for (int i = 0; i < Pop_Size; i++)
oldranseti[i].shuchu();
}
double Evaluate(int k)//计算染色体适应值的函数，计算路径距离
{
double distancer=0;
for (int j = 0; j < citycount-1; j++)
distancer += Map_City.distance[oldranseti[k].X[j]][oldranseti[k].X[j + 1]];
distancer += Map_City.distance[oldranseti[k].X[citycount -1]][oldranseti[k].X[0]];
oldranseti[k].fitness = distancer;
return distancer;
}
void CalculateLeijiProb()
{
double Sumfitness = -0;
for (int i = 0; i < Pop_Size; i++)
oldranseti[i].fitness = Evaluate(i);
for (int i = 0; i < Pop_Size; i++)
Sumfitness += oldranseti[i].fitness;//计算种群中所有染色体的适应值之和
double *SelectProb;
SelectProb=new double[Pop_Size];
for (int i = 0; i < Pop_Size; i++)
SelectProb[i] = oldranseti[i].fitness / Sumfitness;//计算每个染色体的选择概率
for (int i = 0; i < Pop_Size; i++)
{
Leiji_Prob[i] = 0;
for (int j = 0; j <= i; j++)
Leiji_Prob[i] += SelectProb[j];//计算每个染色体的累积概率
}
}
void Roulette()
{
for (int i = 0; i < Pop_Size; i++)//轮盘赌选择产生新种群
{
double suijishu = u0(random);//产生一个0-1之间随机数
int j = 0;
for (; j < Pop_Size; j++)
{
if (suijishu <= Leiji_Prob[j])break;
}
ranseti[i].Init();
for(int k=0;k<citycount;k++)
ranseti[i].X[k] = oldranseti[j].X[k];
}
for (int i = 0; i < Pop_Size; i++)
{
ranseti[i].fitness = 0;
for (int j = 0; j < citycount - 1; j++)
ranseti[i].fitness += Map_City.distance[ranseti[i].X[j]][ranseti[i].X[j + 1]];
ranseti[i].fitness += Map_City.distance[ranseti[i].X[citycount - 1]][ranseti[i].X[0]];
}
}
void Cross(double Pc)//交叉函数采用部分匹配交叉策略
{
for (int k = 0; k+1 < Pop_Size; k = k + 2)
{
int k1 = k;
int k2 = k1 + 1;
double suijishu = u0(random);
if (Pc > suijishu)
{
int pos1 = rand() % citycount, pos2 = rand() % citycount;
if (pos1 > pos2)//确保pos1小于pos2
{
pos1 += pos2;
pos2 = pos1 - pos2;
pos1 = pos1 - pos2;
}
for (int j = 0; j <citycount; j++)
{
if (j >= pos1 && j <= pos2)
{
ranseti[k1].X[j]= ranseti[k1].X[j]+ranseti[k2].X[j];
ranseti[k2].X[j] = ranseti[k1].X[j] - ranseti[k2].X[j];
ranseti[k1].X[j] = ranseti[k1].X[j] - ranseti[k2].X[j];
}
}
int cishu = pos2 - pos1 + 1;
for (int j = 0; j<citycount; j++)
{
if ((j<pos1) || (j>pos2))
{
for (int jishu = 1; jishu <= cishu; jishu++)
{
for (int m = pos1; m <= pos2; m++)
{
if (ranseti[k1].X[j] == ranseti[k1].X[m])
{
ranseti[k1].X[j] = ranseti[k2].X[m];
}
}
}
}
}
for (int j = 0; j<citycount; j++)
{
if ((j<pos1) || (j>pos2))
{
for (int jishu = 1; jishu <= cishu; jishu++)
{
for (int m = pos1; m <= pos2; m++)
{
if (ranseti[k2].X[j] == ranseti[k2].X[m])
{
ranseti[k2].X[j] = ranseti[k1].X[m];
}
}
}
}
}
}
}
for (int i = 0; i < Pop_Size; i++)
{
ranseti[i].fitness = 0;
for (int j = 0; j < citycount - 1; j++)
ranseti[i].fitness += Map_City.distance[ranseti[i].X[j]][ranseti[i].X[j + 1]];
ranseti[i].fitness += Map_City.distance[ranseti[i].X[citycount - 1]][ranseti[i].X[0]];
}
}
void Bianyi(double Pm)//变异函数
{
for (int k = 0; k < Pop_Size; k++)
{
double suijishu = u0(random);
if (Pm > suijishu)
{
int pos1 = rand() % citycount, pos2 = rand() % citycount;//随机选出两个变异位置，然后将两个位置上的城市序号进行交换
while (pos2 == pos1)
pos2 = rand() % citycount;
ranseti[k].X[pos1] += ranseti[k].X[pos2];
ranseti[k].X[pos2] = ranseti[k].X[pos1] - ranseti[k].X[pos2];
ranseti[k].X[pos1] = ranseti[k].X[pos1] - ranseti[k].X[pos2];
}
ranseti[k].fitness = 0;
for (int j = 0; j < citycount - 1; j++)
ranseti[k].fitness += Map_City.distance[ranseti[k].X[j]][ranseti[k].X[j + 1]];
ranseti[k].fitness += Map_City.distance[ranseti[k].X[citycount - 1]][ranseti[k].X[0]];
}
}
void SelectBestRanseti()
{
for (int i = 0; i < Pop_Size; i++)
{
if (ranseti[i].fitness < Bestranseti.fitness)
{
for (int j = 0; j < citycount; j++)
Bestranseti.X[j] = ranseti[i].X[j];
}
}
Bestranseti.fitness = 0;
for (int j = 0; j < citycount - 1; j++)
Bestranseti.fitness += Map_City.distance[Bestranseti.X[j]][Bestranseti.X[j + 1]];
Bestranseti.fitness += Map_City.distance[Bestranseti.X[citycount - 1]][Bestranseti.X[0]];
}
void GA_TSP()
{
ofstream outfile;
outfile.open("result.txt",ios::trunc);
for (int k = 0; k < Itetime; k++)
{
outfile << "第" << k + 1 << "代种群：" << endl;
CalculateLeijiProb();//计算上一代种群染色体的累积概率
Roulette();//轮盘赌法选择出下一代种群
SelectBestRanseti();//获得最好个体
outfile << "轮盘赌选择后的新种群：" << endl;
for (int i = 0; i < Pop_Size; i++)
for (int j = 0; j < citycount; j++)
{
if(j== citycount -1)outfile << ranseti[i].X[j] <<" "<<ranseti[i].fitness << " " << endl;
else outfile << ranseti[i].X[j]<<"->";
}
Cross(Cross_Prob);
SelectBestRanseti();//获得最好个体
outfile << "交叉后的新种群：" << endl;
for (int i = 0; i < Pop_Size; i++)
for (int j = 0; j < citycount; j++)
{
if (j == citycount - 1)outfile << ranseti[i].X[j] << " " << ranseti[i].fitness <<" "<< endl;
else outfile << ranseti[i].X[j] << "->";
}
Bianyi(Bianyi_Prob);
SelectBestRanseti();//获得最好个体
outfile << "变异后的新种群：" << "\n";
for (int i = 0; i < Pop_Size; i++)
for (int j = 0; j < citycount; j++)
{
if (j ==citycount - 1)outfile << ranseti[i].X[j] << " " << ranseti[i].fitness << " " << endl;
else outfile << ranseti[i].X[j] << "->";
}
SelectBestRanseti();//获得最好个体
std::cout << "第" << k + 1 << "次迭代的最优路径长度为：" << Bestranseti.fitness<<endl;
outfile << "第" << k + 1 << "次迭代的最优路径长度为：" << Bestranseti.fitness << endl;
for (int i = 0; i < Pop_Size; i++)
{
for (int j = 0; j < citycount; j++)
{
oldranseti[i].X[j] = ranseti[i].X[j];
}
oldranseti[i].fitness = ranseti[i].fitness;
}
}
std::cout<<"****************迭代结束！****************"<<endl;
std::cout << Itetime << "次迭代结束后的最优路径长度为：" << Bestranseti.fitness<<endl;
std::cout << Itetime << "次迭代结束后的最优个体如下：" << endl;
outfile << Itetime << "次迭代结束后的最优路径长度为：" << Bestranseti.fitness << endl;
outfile << Itetime << "次迭代结束后的最优个体如下：" << endl;
for (int j = 0; j < citycount; j++)
{
if (j == citycount - 1)
{
std::cout << Bestranseti.X[j] << endl;
outfile << Bestranseti.X[j] << endl;
}
else
{
std::cout << Bestranseti.X[j] << "->";
outfile << Bestranseti.X[j] << "->";
}
}
outfile.close();
}
};
4 、自定义函数
4.1 四舍五入取整函数
double round(double r){
return (r > 0.0) ? floor(r + 0.5) : ceil(r - 0.5);}
4.2 随机生成1~n的一个排列的函数
int * Random_N(int n)
{
int *geti;
geti = new int[n];
int j = 0;
while(j<n)
{
while (true)
{
int flag = -1;
int temp = rand() % n + 1;
if (j > 0)
{
int k = 0;
for(; k < j; k++)
{
if (temp == *(geti + k))break;
}
if (k == j)
{
*(geti + j) = temp;
flag = 1;
}
}
else
{
*(geti + j) = temp;
flag = 1;
}
if (flag == 1)break;
}
j++;
}
return geti;
}
5 、全局变量
std::default_random_engine random((unsigned int)time(NULL));
std::uniform_real_distribution<double> u0(0, 1); //随机数分布对象
const int citycount = 29;//城市的数量作为全局常量，需提前在代码中赋值
Graph Map_City;//定义全局对象图,放在Graph类后
GA zq;//种群类，放在GA类后
6 、主函数
int main()
{
std::cout << "****************遗传算法求解旅行商问题！****************" << endl;
std::cout << "****************城市数量："<<citycount <<"****************"<< endl;
zq.Init(50,100,0.8,0.9, "E:\\下学期课程\\Matlab智能计算\\GA_TSP\\GA_TSP\\bayg29.tsp");
std::cout<<"****************初始化后的种群如下：****************"<<endl;
zq.shuchu();
zq.GA_TSP();
system("pause");
return 0;
}
7 、运行结果
7.1 控制台运行结果：
7.2 生成的result.txt文件结果：
附录（完整代码）
#include <iostream>
#include <fstream>
#include <string>
#include <time.h>
#include <random>
using namespace std;
std::default_random_engine random((unsigned int)time(NULL));
std::uniform_real_distribution<double> u0(0, 1); //随机数分布对象
const int citycount = 29;//城市的数量
class City
{
public:
string name;//城市名称
double x, y;//城市点的二维坐标
void shuchu()
{
std::cout <<name+":"<<"("<< x << "," << y <<")"<< endl;
}
};
double round(double r){
return (r > 0.0) ? floor(r + 0.5) : ceil(r - 0.5);}
class Graph
{
public:
City city[citycount];//城市数组
double distance[citycount][citycount];//城市间的距离矩阵
void Readcoordinatetxt(string txtfilename)//读取城市坐标文件的函数
{
ifstream myfile(txtfilename, ios::in);
double x = 0, y = 0;
int z = 0;
if (!myfile.fail())
{
int i = 0;
while (!myfile.eof() && (myfile >> z >> x >> y))
{
city[i].name = to_string(_Longlong(z));//城市名称转化为字符串
city[i].x = x; city[i].y = y;
i++;
}
}
else
cout << "文件不存在";
myfile.close();//计算城市距离矩阵
for (int i = 0; i < citycount; i++)
for (int j = 0; j < citycount; j++)
{
distance[i][j] = sqrt((pow((city[i].x - city[j].x), 2) + pow((city[i].y - city[j].y), 2))/10.0);//计算城市ij之间的伪欧式距离
if (round(distance[i][j] < distance[i][j]))distance[i][j] = round(distance[i][j]) + 1;
else distance[i][j] = round(distance[i][j]);
}
}
void shuchu()
{
cout << "城市名称 " << "坐标x" << " " << "坐标y" << endl;
for (int i = 0; i < citycount; i++)
city[i].shuchu();
cout << "距离矩阵： " << endl;
for (int i = 0; i < citycount; i++)
{
for (int j = 0; j < citycount; j++)
{
if (j == citycount - 1)
std::cout << distance[i][j] << endl;
else
std::cout << distance[i][j] << " ";
}
}
}
};
Graph Map_City;//定义全局对象图
int * Random_N(int n)
{
int *geti;
geti = new int[n];
int j = 0;
while(j<n)
{
while (true)
{
int flag = -1;
int temp = rand() % n + 1;
if (j > 0)
{
int k = 0;
for(; k < j; k++)
{
if (temp == *(geti + k))break;
}
if (k == j)
{
*(geti + j) = temp;
flag = 1;
}
}
else
{
*(geti + j) = temp;
flag = 1;
}
if (flag == 1)break;
}
j++;
}
return geti;
}
class Ranseti
{
public:
int *X;
double fitness;
void Init()
{
X = new int[citycount];
int *M = Random_N(citycount);
for (int j = 0; j < citycount; j++)
X[j] = *(M + j);
fitness=0;
}
void shuchu()
{
for (int j = 0; j < citycount; j++)
{
if (j == citycount -1) std::cout << X[j] << " "<<fitness<<endl;
else std::cout << X[j] << "->";
}
}
};
class GA
{
public:
Ranseti *oldranseti;//上一代种群
Ranseti *ranseti;//下一代种群
int Pop_Size;//种群大小
int Itetime;//迭代次数
double Cross_Prob;//交叉概率
double Bianyi_Prob;//变异概率
Ranseti Bestranseti;//最佳染色体个体
double Bestlength;//最短路径的长度
double *Leiji_Prob;//累积选择概率
void Init(int popsize,int itetime,double crossprob,double bianyiprob,string filename)
{
Map_City.Readcoordinatetxt(filename);
Map_City.shuchu();
Pop_Size = popsize;
Itetime = itetime;
Cross_Prob = crossprob;
Bianyi_Prob = bianyiprob;
oldranseti = new Ranseti[Pop_Size];
ranseti = new Ranseti[Pop_Size];
Leiji_Prob = new double[Pop_Size];
for (int i = 0; i < Pop_Size; i++)
{
oldranseti[i].Init();
oldranseti[i].fitness = Evaluate(i);//初始化适应度
}
int bestid = -1;
for (int i = 0; i < Pop_Size; i++)
{
int j = 0;
for (; j < Pop_Size; j++)
{
if (oldranseti[i].fitness > oldranseti[j].fitness)break;
}
if (j == Pop_Size)
{
bestid = i;
break;
}
}
Bestranseti.Init();
Bestranseti.fitness = 0;
for (int j = 0; j < citycount; j++)
Bestranseti.X[j] = oldranseti[bestid].X[j];
for (int j = 0; j < citycount - 1; j++)
Bestranseti.fitness += Map_City.distance[Bestranseti.X[j]][Bestranseti.X[j + 1]];
Bestranseti.fitness += Map_City.distance[Bestranseti.X[citycount - 1]][Bestranseti.X[0]];
Bestlength = Bestranseti.fitness;
}
void shuchu()
{
for (int i = 0; i < Pop_Size; i++)
oldranseti[i].shuchu();
}
double Evaluate(int k)//计算染色体适应值的函数，计算路径距离
{
double distancer=0;
for (int j = 0; j < citycount-1; j++)
distancer += Map_City.distance[oldranseti[k].X[j]][oldranseti[k].X[j + 1]];
distancer += Map_City.distance[oldranseti[k].X[citycount -1]][oldranseti[k].X[0]];
oldranseti[k].fitness = distancer;
return distancer;
}
void CalculateLeijiProb()
{
double Sumfitness = -0;
for (int i = 0; i < Pop_Size; i++)
oldranseti[i].fitness = Evaluate(i);
for (int i = 0; i < Pop_Size; i++)
Sumfitness += oldranseti[i].fitness;//计算种群中所有染色体的适应值之和
double *SelectProb;
SelectProb=new double[Pop_Size];
for (int i = 0; i < Pop_Size; i++)
SelectProb[i] = oldranseti[i].fitness / Sumfitness;//计算每个染色体的选择概率
for (int i = 0; i < Pop_Size; i++)
{
Leiji_Prob[i] = 0;
for (int j = 0; j <= i; j++)
Leiji_Prob[i] += SelectProb[j];//计算每个染色体的累积概率
}
}
void Roulette()
{
for (int i = 0; i < Pop_Size; i++)//轮盘赌选择产生新种群
{
double suijishu = u0(random);//产生一个0-1之间随机数
int j = 0;
for (; j < Pop_Size; j++)
{
if (suijishu <= Leiji_Prob[j])break;
}
ranseti[i].Init();
for(int k=0;k<citycount;k++)
ranseti[i].X[k] = oldranseti[j].X[k];
}
for (int i = 0; i < Pop_Size; i++)
{
ranseti[i].fitness = 0;
for (int j = 0; j < citycount - 1; j++)
ranseti[i].fitness += Map_City.distance[ranseti[i].X[j]][ranseti[i].X[j + 1]];
ranseti[i].fitness += Map_City.distance[ranseti[i].X[citycount - 1]][ranseti[i].X[0]];
}
}
void Cross(double Pc)//交叉函数采用部分匹配交叉策略
{
for (int k = 0; k+1 < Pop_Size; k = k + 2)
{
int k1 = k;
int k2 = k1 + 1;
double suijishu = u0(random);
if (Pc > suijishu)
{
int pos1 = rand() % citycount, pos2 = rand() % citycount;
if (pos1 > pos2)//确保pos1小于pos2
{
pos1 += pos2;
pos2 = pos1 - pos2;
pos1 = pos1 - pos2;
}
for (int j = 0; j <citycount; j++)
{
if (j >= pos1 && j <= pos2)
{
ranseti[k1].X[j]= ranseti[k1].X[j]+ranseti[k2].X[j];
ranseti[k2].X[j] = ranseti[k1].X[j] - ranseti[k2].X[j];
ranseti[k1].X[j] = ranseti[k1].X[j] - ranseti[k2].X[j];
}
}
int cishu = pos2 - pos1 + 1;
for (int j = 0; j<citycount; j++)
{
if ((j<pos1) || (j>pos2))
{
for (int jishu = 1; jishu <= cishu; jishu++)
{
for (int m = pos1; m <= pos2; m++)
{
if (ranseti[k1].X[j] == ranseti[k1].X[m])
{
ranseti[k1].X[j] = ranseti[k2].X[m];
}
}
}
}
}
for (int j = 0; j<citycount; j++)
{
if ((j<pos1) || (j>pos2))
{
for (int jishu = 1; jishu <= cishu; jishu++)
{
for (int m = pos1; m <= pos2; m++)
{
if (ranseti[k2].X[j] == ranseti[k2].X[m])
{
ranseti[k2].X[j] = ranseti[k1].X[m];
}
}
}
}
}
}
}
for (int i = 0; i < Pop_Size; i++)
{
ranseti[i].fitness = 0;
for (int j = 0; j < citycount - 1; j++)
ranseti[i].fitness += Map_City.distance[ranseti[i].X[j]][ranseti[i].X[j + 1]];
ranseti[i].fitness += Map_City.distance[ranseti[i].X[citycount - 1]][ranseti[i].X[0]];
}
}
void Bianyi(double Pm)//变异函数
{
for (int k = 0; k < Pop_Size; k++)
{
double suijishu = u0(random);
if (Pm > suijishu)
{
int pos1 = rand() % citycount, pos2 = rand() % citycount;//随机选出两个变异位置，然后将两个位置上的城市序号进行交换
while (pos2 == pos1)
pos2 = rand() % citycount;
ranseti[k].X[pos1] += ranseti[k].X[pos2];
ranseti[k].X[pos2] = ranseti[k].X[pos1] - ranseti[k].X[pos2];
ranseti[k].X[pos1] = ranseti[k].X[pos1] - ranseti[k].X[pos2];
}
ranseti[k].fitness = 0;
for (int j = 0; j < citycount - 1; j++)
ranseti[k].fitness += Map_City.distance[ranseti[k].X[j]][ranseti[k].X[j + 1]];
ranseti[k].fitness += Map_City.distance[ranseti[k].X[citycount - 1]][ranseti[k].X[0]];
}
}
void SelectBestRanseti()
{
for (int i = 0; i < Pop_Size; i++)
{
if (ranseti[i].fitness < Bestranseti.fitness)
{
for (int j = 0; j < citycount; j++)
Bestranseti.X[j] = ranseti[i].X[j];
}
}
Bestranseti.fitness = 0;
for (int j = 0; j < citycount - 1; j++)
Bestranseti.fitness += Map_City.distance[Bestranseti.X[j]][Bestranseti.X[j + 1]];
Bestranseti.fitness += Map_City.distance[Bestranseti.X[citycount - 1]][Bestranseti.X[0]];
}
void GA_TSP()
{
ofstream outfile;
outfile.open("result.txt",ios::trunc);
for (int k = 0; k < Itetime; k++)
{
outfile << "第" << k + 1 << "代种群：" << endl;
CalculateLeijiProb();//计算上一代种群染色体的累积概率
Roulette();//轮盘赌法选择出下一代种群
SelectBestRanseti();//获得最好个体
outfile << "轮盘赌选择后的新种群：" << endl;
for (int i = 0; i < Pop_Size; i++)
for (int j = 0; j < citycount; j++)
{
if(j== citycount -1)outfile << ranseti[i].X[j] <<" "<<ranseti[i].fitness << " " << endl;
else outfile << ranseti[i].X[j]<<"->";
}
Cross(Cross_Prob);
SelectBestRanseti();//获得最好个体
outfile << "交叉后的新种群：" << endl;
for (int i = 0; i < Pop_Size; i++)
for (int j = 0; j < citycount; j++)
{
if (j == citycount - 1)outfile << ranseti[i].X[j] << " " << ranseti[i].fitness <<" "<< endl;
else outfile << ranseti[i].X[j] << "->";
}
Bianyi(Bianyi_Prob);
SelectBestRanseti();//获得最好个体
outfile << "变异后的新种群：" << "\n";
for (int i = 0; i < Pop_Size; i++)
for (int j = 0; j < citycount; j++)
{
if (j ==citycount - 1)outfile << ranseti[i].X[j] << " " << ranseti[i].fitness << " " << endl;
else outfile << ranseti[i].X[j] << "->";
}
SelectBestRanseti();//获得最好个体
std::cout << "第" << k + 1 << "次迭代的最优路径长度为：" << Bestranseti.fitness<<endl;
outfile << "第" << k + 1 << "次迭代的最优路径长度为：" << Bestranseti.fitness << endl;
for (int i = 0; i < Pop_Size; i++)
{
for (int j = 0; j < citycount; j++)
{
oldranseti[i].X[j] = ranseti[i].X[j];
}
oldranseti[i].fitness = ranseti[i].fitness;
}
}
std::cout<<"****************迭代结束！****************"<<endl;
std::cout << Itetime << "次迭代结束后的最优路径长度为：" << Bestranseti.fitness<<endl;
std::cout << Itetime << "次迭代结束后的最优个体如下：" << endl;
outfile << Itetime << "次迭代结束后的最优路径长度为：" << Bestranseti.fitness << endl;
outfile << Itetime << "次迭代结束后的最优个体如下：" << endl;
for (int j = 0; j < citycount; j++)
{
if (j == citycount - 1)
{
std::cout << Bestranseti.X[j] << endl;
outfile << Bestranseti.X[j] << endl;
}
else
{
std::cout << Bestranseti.X[j] << "->";
outfile << Bestranseti.X[j] << "->";
}
}
outfile.close();
}
};
GA zq;
int main()
{
std::cout << "****************遗传算法求解旅行商问题！****************" << endl;
std::cout << "****************城市数量："<<citycount <<"****************"<< endl;
zq.Init(50,100,0.8,0.9, "E:\\下学期课程\\Matlab智能计算\\GA_TSP\\GA_TSP\\bayg29.tsp");
std::cout<<"****************初始化后的种群如下：****************"<<endl;
zq.shuchu();
zq.GA_TSP();
system("pause");
return 0;
}