不知道现在是否还有人在研究蚁群算法？应该有吧。当初为了找C或C++源代码而不可得，在阅读了多份”杂七杂八”的代码的基础，总算写出来了。运行了三个TSP经典用例，基本符合要求。2008年3月份写的，现在贴出来大家共享一下，注释加的应该算齐全。仅仅是一份按照蚁群算法的原理写的代码，没有做任何优化。至于我做优化后的代码，就不发出来了吧，呵呵。环境为:Windows XP SP2 + VC 6.0.
/********************************* *作者:陈杰 *单位:四川大学计算机学院 *邮件地址:scucj@126.com *完成时间:2008年3月 *********************************/ #include<iostream> #include<math.h> #include<time.h> using namespace std; //该程序是以蚁群系统为模型写的蚁群算法程序(强调：非蚂蚁周模型)，以三个著名的TSP问题为测试对象 //通过微调参数，都可以获得较好的解 /* //———-(1)问题一：Oliver 30 城市 TSP 问题 best_length = 423.7406; ———————— //该程序最好的结果是423.741，可运行多次获得 //城市节点数目 #define N 30 //城市坐标 double C[N][2]={ {2,99},{4,50},{7,64},{13,40},{18,54},{18,40},{22,60},{24,42},{25,62},{25,38}, {37,84},{41,94},{41,26},{44,35},{45,21},{54,67},{54,62},{58,35},{58,69},{62,32}, {64,60},{68,58},{71,44},{71,71},{74,78},{82,7},{83,46},{83,69},{87,76},{91,38} }; //———-上面参数是固定的，下面的参数是可变的———– //蚂蚁数量 #define M 30 //最大循环次数NcMax int NcMax = 500; //信息启发因子，期望启发式因子，全局信息素挥发参数，局部信息素挥发参数, 状态转移公式中的q0 double alpha = 2, beta = 3, rou = 0.1, alpha1 = 0.1, qzero = 0.01; //———–问题一结束———————————————————————— */ /* //———-(2)问题二：Elion50 城市 TSP 问题 best_length = 427.96; —————————- //该程序最好的结果是428.468，可运行多次获得 //城市节点数目 #define N 50 //城市坐标 double C[N][2]={ {5,64}, {5,25}, {5,6}, {7,38}, {8,52}, {10,17}, {12,42}, {13,13}, {16,57}, {17,33}, {17,63}, {20,26}, {21,47}, {21,10}, {25,32}, {25,55}, {27,68}, {27,23}, {30,48}, {30,15}, {31,62}, {31,32}, {32,22}, {32,39}, {36,16}, {37,69}, {37,52}, {38,46}, {39,10}, {40,30}, {42,57}, {42,41}, {43,67}, {45,35}, {46,10}, {48,28}, {49,49}, {51,21}, {52,33}, {52,41}, {52,64}, {56,37}, {57,58}, {58,27}, {58,48}, {59,15}, {61,33}, {62,42}, {62,63}, {63,69} }; //———-上面参数是固定的，下面的参数是可变的———– //蚂蚁数量 #define M 50 //最大循环次数NcMax int NcMax = 1000; //信息启发因子，期望启发式因子，全局信息素挥发参数，局部信息素挥发参数, 状态转移公式中的q0 double alpha = 2, beta = 4, rou = 0.1, alpha1 = 0.1, qzero = 0.01; //———–问题二结束———————————————————————— */ //———-(3)问题三：Elion75 城市 TSP 问题 best_length = 542.31; //该程序最好的结果是542.309，可运行多次获得 //城市节点数目 #define N 75 //城市坐标 double C[N][2]={ {6,25}, {7,43}, {9,56}, {10,70}, {11,28}, {12,17}, {12,38}, {15,5}, {15,14}, {15,56}, {16,19}, {17,64}, {20,30}, {21,48}, {21,45}, {21,36}, {22,53}, {22,22}, {26,29}, {26,13}, {26,59}, {27,24}, {29,39}, {30,50}, {30,20}, {30,60}, {31,76}, {33,34}, {33,44}, {35,51}, {35,16}, {35,60}, {36,6}, {36,26}, {38,33}, {40,37}, {40,66}, {40,60}, {40,20}, {41,46}, {43,26}, {44,13}, {45,42}, {45,35}, {47,66}, {48,21}, {50,30}, {50,40}, {50,50}, {50,70}, {50,4}, {50,15}, {51,42}, {52,26}, {54,38}, {54,10}, {55,34}, {55,45}, {55,50}, {55,65}, {55,57}, {55,20}, {57,72}, {59,5}, {60,15}, {62,57}, {62,48}, {62,35}, {62,24}, {64,4}, {65,27}, {66,14}, {66,8}, {67,41}, {70,64} }; //———-上面参数是固定的，下面的参数是可变的———– //蚂蚁数量 #define M 75 //最大循环次数NcMax int NcMax =1000; //信息启发因子，期望启发式因子，全局信息素挥发参数，局部信息素挥发参数, 状态转移公式中的q0 double alpha = 2, beta = 5, rou = 0.1, alpha1 = 0.1, qzero = 0.1; //———–问题三结束———————————————————————— //=========================================================================================================== //局部更新时候使用的的常量，它是由最近邻方法得到的一个长度 //什么是最近邻方法?:)就是从源节点出发，每次选择一个距离最短的点来遍历所有的节点得到的路径 //每个节点都可能作为源节点来遍历 double Lnn; //矩阵表示两两城市之间的距离 double allDistance[N][N]; //计算两个城市之间的距离 double calculateDistance(int i, int j) { return sqrt(pow((C[i][0]-C[j][0]),2.0) + pow((C[i][1]-C[j][1]),2.0)); } //由矩阵表示两两城市之间的距离 void calculateAllDistance() { for(int i = 0; i < N; i++) { for(int j = 0; j < N; j++) { if (i != j) { allDistance[i][j] = calculateDistance(i, j); allDistance[j][i] = allDistance[i][j]; } } } } //获得经过n个城市的路径长度 double calculateSumOfDistance(int* tour) { double sum = 0; for(int i = 0; i< N ;i++) { int row = *(tour + 2 * i); int col = *(tour + 2* i + 1); sum += allDistance[row][col]; } return sum; } class ACSAnt; class AntColonySystem { private: double info[N][N], visible[N][N];//节点之间的信息素强度,节点之间的能见度 public: AntColonySystem() { } //计算当前节点到下一节点转移的概率 double Transition(int i, int j); //局部更新规则 void UpdateLocalPathRule(int i, int j); //初始化 void InitParameter(double value); //全局信息素更新 void UpdateGlobalPathRule(int* bestTour, int globalBestLength); }; //计算当前节点到下一节点转移的概率 double AntColonySystem::Transition(int i, int j) { if (i != j) { return (pow(info[i][j],alpha) * pow(visible[i][j], beta)); } else { return 0.0; } } //局部更新规则 void AntColonySystem::UpdateLocalPathRule(int i, int j) { info[i][j] = (1.0 – alpha1) * info[i][j] + alpha1 * (1.0 / (N * Lnn)); info[j][i] = info[i][j]; } //初始化 void AntColonySystem::InitParameter(double value) { //初始化路径上的信息素强度tao0 for(int i = 0; i < N; i++) { for(int j = 0; j < N; j++) { info[i][j] = value; info[j][i] = value; if (i != j) { visible[i][j] = 1.0 / allDistance[i][j]; visible[j][i] = visible[i][j]; } } } } //全局信息素更新 void AntColonySystem::UpdateGlobalPathRule(int* bestTour, int globalBestLength) { for(int i = 0; i < N; i++) { int row = *(bestTour + 2 * i); int col = *(bestTour + 2* i + 1); info[row][col] = (1.0 – rou) * info[row][col] + rou * (1.0 / globalBestLength); info[col][row] =info[row][col]; } } class ACSAnt { private: AntColonySystem* antColony; protected: int startCity, cururentCity;//初始城市编号，当前城市编号 int allowed[N];//禁忌表 int Tour[N][2];//当前路径 int currentTourIndex;//当前路径索引，从0开始，存储蚂蚁经过城市的编号 public: ACSAnt(AntColonySystem* acs, int start) { antColony = acs; startCity = start; } //开始搜索 int* Search(); //选择下一节点 int Choose(); //移动到下一节点 void MoveToNextCity(int nextCity); }; //开始搜索 int* ACSAnt::Search() { cururentCity = startCity; int toCity; currentTourIndex = 0; for(int i = 0; i < N; i++) { allowed[i] = 1; } allowed[cururentCity] = 0; int endCity; int count = 0; do { count++; endCity = cururentCity; toCity = Choose(); if (toCity >= 0) { MoveToNextCity(toCity); antColony->UpdateLocalPathRule(endCity, toCity); cururentCity = toCity; } }while(toCity >= 0); MoveToNextCity(startCity); antColony->UpdateLocalPathRule(endCity, startCity); return *Tour; } //选择下一节点 int ACSAnt::Choose() { int nextCity = -1; double q = rand()/(double)RAND_MAX; //如果 q <= q0,按先验知识，否则则按概率转移， if (q <= qzero) { double probability = -1.0;//转移到下一节点的概率 for(int i = 0; i < N; i++) { //去掉禁忌表中已走过的节点,从剩下节点中选择最大概率的可行节点 if (1 == allowed[i]) { double prob = antColony->Transition(cururentCity, i); if (prob > probability) { nextCity = i; probability = prob; } } } } else { //按概率转移 double p = rand()/(double)RAND_MAX;//生成一个随机数,用来判断落在哪个区间段 double sum = 0.0; double probability = 0.0;//概率的区间点，p 落在哪个区间段，则该点是转移的方向 //计算概率公式的分母的值 for(int i = 0; i < N; i++) { if (1 == allowed[i]) { sum += antColony->Transition(cururentCity, i); } } for(int j = 0; j < N; j++) { if (1 == allowed[j] && sum > 0) { probability += antColony->Transition(cururentCity, j)/sum; if (probability >= p || (p > 0.9999 && probability > 0.9999)) { nextCity = j; break; } } } } return nextCity; } //移动到下一节点 void ACSAnt::MoveToNextCity(int nextCity) { allowed[nextCity]=0; Tour[currentTourIndex][0] = cururentCity; Tour[currentTourIndex][1] = nextCity; currentTourIndex++; cururentCity = nextCity; } //—————————————— //选择下一个节点，配合下面的函数来计算的长度 int ChooseNextNode(int currentNode, int visitedNode[]) { int nextNode = -1; double shortDistance = 0.0; for(int i = 0; i < N; i++) { //去掉已走过的节点,从剩下节点中选择距离最近的节点 if (1 == visitedNode[i]) { if (shortDistance == 0.0) { shortDistance = allDistance[currentNode][i]; nextNode = i; } if(shortDistance < allDistance[currentNode][i]) { nextNode = i; } } } return nextNode; } //给一个节点由最近邻距离方法计算长度 double CalAdjacentDistance(int node) { double sum = 0.0; int visitedNode[N]; for(int j = 0; j < N; j++) { visitedNode[j] = 1; } visitedNode[node] = 0; int currentNode = node; int nextNode; do { nextNode = ChooseNextNode(currentNode, visitedNode); if (nextNode >= 0) { sum += allDistance[currentNode][nextNode]; currentNode= nextNode; visitedNode[currentNode] = 0; } }while(nextNode >= 0); sum += allDistance[currentNode][node]; return sum; } //———————————结束——————————————— //————————–主函数————————————————– int main() { time_t timer,timerl; time(&timer); unsigned long seed = timer; seed %= 56000; srand((unsigned int)seed); //由矩阵表示两两城市之间的距离 calculateAllDistance(); //蚁群系统对象 AntColonySystem* acs = new AntColonySystem(); ACSAnt* ants[M]; //蚂蚁均匀分布在城市上 for(int k = 0; k < M; k++) { ants[k] = new ACSAnt(acs, (int)(k%N)); } calculateAllDistance(); //随机选择一个节点计算由最近邻方法得到的一个长度 int node = rand() % N; Lnn = CalAdjacentDistance(node); //各条路径上初始化的信息素强度 double initInfo = 1 / (N * Lnn); acs->InitParameter(initInfo); //全局最优路径 int globalTour[N][2]; //全局最优长度 double globalBestLength = 0.0; for(int i = 0; i < NcMax; i++) { //局部最优路径 int localTour[N][2]; //局部最优长度 double localBestLength = 0.0; //当前路径长度 double tourLength; for(int j = 0; j < M; j++) { int* tourPath = ants[j]->Search(); tourLength = calculateSumOfDistance(tourPath); //局部比较，并记录路径和长度 if(tourLength < localBestLength || abs(localBestLength – 0.0) < 0.000001) { for(int m = 0; m< N; m++) { int row = *(tourPath + 2 * m); int col = *(tourPath + 2* m + 1); localTour[m][0] = row; localTour[m][1] = col; } localBestLength = tourLength; } } //全局比较，并记录路径和长度 if(localBestLength < globalBestLength || abs(globalBestLength – 0.0) < 0.000001) { for(int m = 0; m< N; m++) { globalTour[m][0] = localTour[m][0]; globalTour[m][1] = localTour[m][1]; } globalBestLength = localBestLength; } acs->UpdateGlobalPathRule(*globalTour, globalBestLength); //输出所有蚂蚁循环一次后的迭代最优路径 cout<<“第 “<<i + 1<<” 迭代最优路径:”<<localBestLength<<“.”<<endl; for(int m = 0; m< N; m++) { cout<<localTour[m][0]<<“.”; } cout<<endl; } //输出全局最优路径 cout<<“全局最优路径长度:”<<globalBestLength<<endl; cout<<“全局最优路径:”; for(int m = 0; m< N; m++) { cout<<globalTour[m][0]<<“.”; } cout<<endl; time(&timerl); int t = timerl – timer; return 0; } //————————–主函数结束————————————————–
源代码下载:
PS:如果大家认为代码写的还算通俗易懂的话，就留个言吧。当然，扔个鸡蛋我也不反对。
