蚁群聚类.zip_MATLAB 聚类_蚁群 聚类_蚁群算法MATLAB_蚁群聚类_蚁群聚类算法

clc; clf; clear; % X = 测试样本矩阵; X = load('data.txt'); [N,n]=size(X); % N =测试样本数;n =测试样本的属性数; K = 4; % K = 组数; R = 100; % R = 蚂蚁数; t_max = 1000; % t_max =最大迭代次数; % 初始化 c = 10^-2; tau = ones(N,K) * c; %信息素矩阵，初始值为0.01的N*K矩阵（样本数*聚类数） q = 0.9; % 阈值q rho = 0.1; % 蒸发率 best_solution_function_value = inf; % 最佳路径度量值（初值为无穷大，该值越小聚类效果越好） tic %计算程序运行时间 t = 1; %=======程序终止条件（下列两个终止条件任选其一）====== % while ((t<=t_max)) %达到最大迭代次数而终止 % while ((best_solution_function_value>=19727)) %达到一定的聚类效果而终止 while ((best_solution_function_value>=19727)) %========================= %路径标识字符：标识每只蚂蚁的路径 solution_string = zeros(R,N+1); for i = 1 : R %以信息素为依据确定蚂蚁的路径 r = rand(1,N); %随机产生值为0-1随机数的1*51的数组 for g = 1 : N if r(g) < q %如果r(g)小于阈值 tau_max = max(tau(g,:)); Cluster_number = find(tau(g,:)==tau_max); %聚类标识数，选择信息素最多的路径 solution_string(i,g) = Cluster_number(1); %确定第i只蚂蚁对第g个样本的路径标识 else %如果r(g)大于阈值,求出各路径信息素占在总信息素的比例，按概率选择路径 sum_p = sum(tau(g,:)); p = tau(g,:) / sum_p; for u = 2 : K p(u) = p(u) + p(u-1); end rr = rand; for s = 1 : K if (rr <= p(s)) Cluster_number = s; solution_string(i,g) = Cluster_number; break; end end end end % 计算聚类中心 weight = zeros(N,K); for h = 1:N %给路径做计算标识 Cluster_index = solution_string(i,h); %类的索引编号 weight(h,Cluster_index) = 1; %对样本选择的类在weight数组的相应位置标1 end cluster_center = zeros(K,n); %聚类中心（聚类数K个中心） for j = 1:K for v = 1:n sum_wx = sum(weight(:,j).*X(:,v)); %各类样本各属性值之和 sum_w = sum(weight(:,j)); %各类样本个数 if sum_w==0 %该类样本数为0，则该类的聚类中心为0 cluster_center(j,v) =0 continue; else %该类样本数不为0，则聚类中心的值取样本属性值的平均值 cluster_center(j,v) = sum_wx/sum_w; end end end % 计算各样本点各属性到其对应的聚类中心的均方差之和，该值存入solution_string的最后一位 F = 0; for j= 1:K for ii = 1:N Temp=0; if solution_string(i,ii)==j; for v = 1:n Temp = ((abs(X(ii,v)-cluster_center(j,v))).^2)+Temp; end Temp = sqrt(Temp); end F = (Temp)+F; end end solution_string(i,end) = F; end %根据F值，把solution_string矩阵升序排序 [fitness_ascend,solution_index] = sort(solution_string(:,end),1); solution_ascend = [solution_string(solution_index,1:end-1) fitness_ascend]; pls = 0.1; %局部寻优阈值pls（相当于变异率） L = 2; % 在L条路径内局部寻优 % 局部寻优程序 solution_temp = zeros(L,N+1); k = 1; while(k <= L) solution_temp(k,:) = solution_ascend(k,:); rp = rand(1,N); %产生一个1*N(51)维的随机数组，某值小于pls则随机改变其对应的路径标识 for i = 1:N if rp(i) <= pls current_cluster_number = setdiff([1:K],solution_temp(k,i)); rrr=randint(1,1,[1,K-1]); change_cluster = current_cluster_number(rrr); solution_temp(k,i) = change_cluster; end end % 计算临时聚类中心 solution_temp_weight = zeros(N,K); for h = 1:N solution_temp_cluster_index = solution_temp(k,h); solution_temp_weight(h,solution_temp_cluster_index) = 1; end solution_temp_cluster_center = zeros(K,n); for j = 1:K for v = 1:n solution_temp_sum_wx = sum(solution_temp_weight(:,j).*X(:,v)); solution_temp_sum_w = sum(solution_temp_weight(:,j)); if solution_temp_sum_w==0 solution_temp_cluster_center(j,v) =0; continue; else solution_temp_cluster_center(j,v) = solution_temp_sum_wx/solution_temp_sum_w; end end end % 计算各样本点各属性到其对应的临时聚类中心的均方差之和Ft； solution_temp_F = 0; for j= 1:K for ii = 1:N st_Temp=0; if solution_temp(k,ii)==j; for v = 1:n st_Temp = ((abs(X(ii,v)-solution_temp_cluster_center(j,v))).^2)+st_Temp; end st_Temp = sqrt(st_Temp); end solution_temp_F = (st_Temp)+solution_temp_F; end end solution_temp(k,end) = solution_temp_F; %根据临时聚类度量调整路径 % 如果 Ft<Fl 则 Fl=Ft ， Sl=St if solution_temp(k,end) <= solution_ascend(k,end) solution_ascend(k,:) = solution_temp(k,:); end if solution_ascend(k,end)<=best_solution_function_value best_solution = solution_ascend(k,:); end k = k+1; end % 用最好的L条路径更新信息数矩阵 tau_F = 0; for j = 1:L tau_F = tau_F + solution_ascend(j,end); end for i = 1 : N tau(i,best_solution(1,i)) = (1 - rho) * tau(i,best_solution(1,i)) + 1/ tau_F; %1/tau_F和rho/tau_F效果都很好 end t=t+1 best_solution_function_value = solution_ascend(1,end); best_solution_function_value end time=toc; %输出程序运行时间 clc t time cluster_center best_solution = solution_ascend(1,1:end-1); IDY=ctranspose(best_solution) best_solution_function_value = solution_ascend(1,end) %分类结果显示 plot3(cluster_center(:,1),cluster_center(:,2),cluster_center(:,3),'o');grid;box title('蚁群聚类结果(R=100,t=1000)') xlabel('X') ylabel('Y') zlabel('Z') YY=[1 2 3 4]; index1 = find(YY(1) == best_solution) index2 = find(YY(2) == best_solution) index3 = find(YY(3) == best_solution) index4 = find(YY(4) == best_solution) line(X(index1,1),X(index1,2),X(index1,3),'linestyle','none','marker','*','color','g'); line(X(index2,1),X(index2,2),X(index2,3),'linestyle','none','marker','*','color','r'); line(X(index3,1),X(index3,2),X(index3,3),'linestyle','none','marker','+','color','b'); line(X(index4,1),X(index4,2),X(index4,3),'linestyle','none','marker','s','color','b'); rotate3d