1.问题描述：
2.部分程序：
clc;
clf;
clear;
% X = 测试样本矩阵;
X1 = load('data.txt');
X = X1(:,1:2);[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>=1000))
%=========================
%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
plot(cluster_center(:,1),cluster_center(:,2),'o');grid;box
title('蚁群聚类结果(R=100,t=1000)')
xlabel('X')
ylabel('Y')
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
3.仿真结论：
D18
标签：end,temp,sum,solution,cluster,算法,群聚,best From： https://blog.51cto.com/u_15815923/5744377