蚁群算法源代码.doc

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/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 CN2= 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 CN2= 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 CN2= 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 allDistanceNN; /计算两个城市之间的距离 double calculateDistance(int i, int j) return sqrt(pow(Ci0-Cj0),2.0) + pow(Ci1-Cj1),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) allDistanceij = calculateDistance(i, j); allDistanceji = allDistanceij; /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 += allDistancerowcol; return sum; class ACSAnt; class AntColonySystem private: double infoNN, visibleNN;/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(infoij,alpha) * pow(visibleij, beta); else return 0.0; /局部更新规则 void AntColonySystem:UpdateLocalPathRule(int i, int j) infoij = (1.0 - alpha1) * infoij + alpha1 * (1.0 / (N * Lnn); infoji = infoij; /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+) infoij = value; infoji = value; if (i != j) visibleij = 1.0 / allDistanceij; visibleji = visibleij; /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); inforowcol = (1.0 - rou) * inforowcol + rou * (1.0 / globalBestLength); infocolrow =inforowcol; class ACSAnt private: AntColonySystem* antColony; protected: int startCity, cururentCity;/初始城市编号，当前城市编号 int allowedN;/禁忌表 int TourN2;/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 = 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 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 Transition(cururentCity, i); for(int j = 0; j 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) allowednextCity=0; TourcurrentTourIndex0 = cururentCity; TourcurrentTourIndex1 = 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 = visitedNodei) if (shortDistance = 0.0) shortDistance = allDistancecurrentNodei; nextNode = i; if(shortDistance allDistancecurrentNodei) 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 visitedNodeN; for(int j = 0; j = 0) sum += allDistancecurrentNodenextNode; currentNode= nextNode; visitedNodecurrentNode = 0; while(nextNode = 0); sum += allDistancecurrentNodenode; 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* antsM; /蚂蚁均匀分布在城市上 for(int k = 0; k InitParameter(initInfo); /全局最优路径 int globalTourN2; /全局最优长度 double globalBestLength = 0.0; for(int i = 0; i NcMax; i+) /局部最优路径 int localTourN2; /局部最优长度 double localBestLength = 0.0; /当前路径长度 double tourLength; for(int j = 0; 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); localTourm0 = row; localTourm1 = 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; mUpdateGlobalPathRule(*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+) coutlocalTourm0.; coutendl; /输出全局最优路径 cout全局最优路径长度:globalBestLengthendl; cout全局最优路径:; for(int m = 0; m N; m+) coutglobalTourm0.; coutendl; time(&timerl); int t = timerl - timer; return 0;