蚁群实例

#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},{5 8,69},{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NcMaxint 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q0double 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NcMaxint 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q0double 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NcMaxint 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q0double 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tao0for(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;}。