蚁群算法.rar_handsomeh78_matlab_群智能_蚁群_蚁群算法

% Ant Colony Optimization for the Clustering % Reference : An ant colony approach for clustering % % Author : Du Yi % % Copyright : http://dy1981.yculblog.com/ % % Data : 31/03/06 clc; clear; % N = number_of_test_sample; N =150; % n = number_of_attribute_of_test_sample; n = 4; % K = number_of_cluster; K = 3; % R = number_of_ants; R = 10; % t_max = MaxIterations; t_max = 1000; % X = test_sample_matrix; X = [ 5.1 3.5 1.4 0.2 4.9 3.0 1.4 0.2 4.7 3.2 1.3 0.2 4.6 3.1 1.5 0.2 5.0 3.6 1.4 0.2 5.4 3.9 1.7 0.4 4.6 3.4 1.4 0.3 5.0 3.4 1.5 0.2 4.4 2.9 1.4 0.2 4.9 3.1 1.5 0.1 5.4 3.7 1.5 0.2 4.8 3.4 1.6 0.2 4.8 3.0 1.4 0.1 4.3 3.0 1.1 0.1 5.8 4.0 1.2 0.2 5.7 4.4 1.5 0.4 5.4 3.9 1.3 0.4 5.1 3.5 1.4 0.3 5.7 3.8 1.7 0.3 5.1 3.8 1.5 0.3 5.4 3.4 1.7 0.2 5.1 3.7 1.5 0.4 4.6 3.6 1.0 0.2 5.1 3.3 1.7 0.5 4.8 3.4 1.9 0.2 5.0 3.0 1.6 0.2 5.0 3.4 1.6 0.4 5.2 3.5 1.5 0.2 5.2 3.4 1.4 0.2 4.7 3.2 1.6 0.2 4.8 3.1 1.6 0.2 5.4 3.4 1.5 0.4 5.2 4.1 1.5 0.1 5.5 4.2 1.4 0.2 4.9 3.1 1.5 0.1 5.0 3.2 1.2 0.2 5.5 3.5 1.3 0.2 4.9 3.1 1.5 0.1 4.4 3.0 1.3 0.2 5.1 3.4 1.5 0.2 5.0 3.5 1.3 0.3 4.5 2.3 1.3 0.3 4.4 3.2 1.3 0.2 5.0 3.5 1.6 0.6 5.1 3.8 1.9 0.4 4.8 3.0 1.4 0.3 5.1 3.8 1.6 0.2 4.6 3.2 1.4 0.2 5.3 3.7 1.5 0.2 5.0 3.3 1.4 0.2 7.0 3.2 4.7 1.4 6.4 3.2 4.5 1.5 6.9 3.1 4.9 1.5 5.5 2.3 4.0 1.3 6.5 2.8 4.6 1.5 5.7 2.8 4.5 1.3 6.3 3.3 4.7 1.6 4.9 2.4 3.3 1.0 6.6 2.9 4.6 1.3 5.2 2.7 3.9 1.4 5.0 2.0 3.5 1.0 5.9 3.0 4.2 1.5 6.0 2.2 4.0 1.0 6.1 2.9 4.7 1.4 5.6 2.9 3.6 1.3 6.7 3.1 4.4 1.4 5.6 3.0 4.5 1.5 5.8 2.7 4.1 1.0 6.2 2.2 4.5 1.5 5.6 2.5 3.9 1.1 5.9 3.2 4.8 1.8 6.1 2.8 4.0 1.3 6.3 2.5 4.9 1.5 6.1 2.8 4.7 1.2 6.4 2.9 4.3 1.3 6.6 3.0 4.4 1.4 6.8 2.8 4.8 1.4 6.7 3.0 5.0 1.7 6.0 2.9 4.5 1.5 5.7 2.6 3.5 1.0 5.5 2.4 3.8 1.1 5.5 2.4 3.7 1.0 5.8 2.7 3.9 1.2 6.0 2.7 5.1 1.6 5.4 3.0 4.5 1.5 6.0 3.4 4.5 1.6 6.7 3.1 4.7 1.5 6.3 2.3 4.4 1.3 5.6 3.0 4.1 1.3 5.5 2.5 4.0 1.3 5.5 2.6 4.4 1.2 6.1 3.0 4.6 1.4 5.8 2.6 4.0 1.2 5.0 2.3 3.3 1.0 5.6 2.7 4.2 1.3 5.7 3.0 4.2 1.2 5.7 2.9 4.2 1.3 6.2 2.9 4.3 1.3 5.1 2.5 3.0 1.1 5.7 2.8 4.1 1.3 6.3 3.3 6.0 2.5 5.8 2.7 5.1 1.9 7.1 3.0 5.9 2.1 6.3 2.9 5.6 1.8 6.5 3.0 5.8 2.2 7.6 3.0 6.6 2.1 4.9 2.5 4.5 1.7 7.3 2.9 6.3 1.8 6.7 2.5 5.8 1.8 7.2 3.6 6.1 2.5 6.5 3.2 5.1 2.0 6.4 2.7 5.3 1.9 6.8 3.0 5.5 2.1 5.7 2.5 5.0 2.0 5.8 2.8 5.1 2.4 6.4 3.2 5.3 2.3 6.5 3.0 5.5 1.8 7.7 3.8 6.7 2.2 7.7 2.6 6.9 2.3 6.0 2.2 5.0 1.5 6.9 3.2 5.7 2.3 5.6 2.8 4.9 2.0 7.7 2.8 6.7 2.0 6.3 2.7 4.9 1.8 6.7 3.3 5.7 2.1 7.2 3.2 6.0 1.8 6.2 2.8 4.8 1.8 6.1 3.0 4.9 1.8 6.4 2.8 5.6 2.1 7.2 3.0 5.8 1.6 7.4 2.8 6.1 1.9 7.9 3.8 6.4 2.0 6.4 2.8 5.6 2.2 6.3 2.8 5.1 1.5 6.1 2.6 5.6 1.4 7.7 3.0 6.1 2.3 6.3 3.4 5.6 2.4 6.4 3.1 5.5 1.8 6.0 3.0 4.8 1.8 6.9 3.1 5.4 2.1 6.7 3.1 5.6 2.4 6.9 3.1 5.1 2.3 5.8 2.7 5.1 1.9 6.8 3.2 5.9 2.3 6.7 3.3 5.7 2.5 6.7 3.0 5.2 2.3 6.3 2.5 5.0 1.9 6.5 3.0 5.2 2.0 6.2 3.4 5.4 2.3 5.9 3.0 5.1 1.8 ]; % INITIALIZATION % pheromone trails c = 10^-2; tau = ones(N,K) * c; %tau = [ % 0.014756 0.015274 0.009900; % 0.015274 0.009900 0.014756; % 0.015274 0.014756 0.009900; % 0.009900 0.015274 0.014756; % 0.014756 0.015274 0.009900; % 0.009900 0.014756 0.015274; % 0.009900 0.020131 0.009900; % 0.015274 0.014756 0.009900; %]; % probability threshold q q = 0.9; % evaporation rate rho = 0.1; % fitness function best_solution_function_value = inf; t = 1; while ((t <= t_max)) % SEND R ANTS EACH WITH EMPTY SOLUTION STRING,S ============================= % solution string : every ant construction solution and fitness function solution_string = zeros(R,N+1); % COMPUTE R SOLUTION for i = 1 : R % solution_string(1,1:N) = randint(1,N,[1,K]); r = rand(1,N); %construct solution Si using pheromone trail for g = 1 : N if r(g) < q tau_max = max(tau(g,:)); Cluster_number = find(tau(g,:)==tau_max); solution_string(i,g) = Cluster_number(1); %%%%%%%%%%%%% 1--i else sum_p = sum(tau(g,:)); p = tau(g,:) / sum_p; for u = 2 : K p(u) = p(u) + p(u-1); end rr = rand; %Cluster_number = K; for s = 1 : K if (rr <= p(s)) Cluster_number = s; solution_string(i,g) = Cluster_number; %%%%%%%%%%%%% 1--i break; end end end end % compute weights(weight) of all test samples and cluster centers(cluster_center) weight = zeros(N,K); for h = 1:N Cluster_index = solution_string(i,h); %%%%%%%%%%%%% 1--i weight(h,Cluster_index) = 1; end cluster_center = zeros(K,n); for j = 1:K for v = 1:n sum_wx = sum(weight(:,j).*X(:,v)); sum_w = sum(weight(:,j)); if sum_w==0 cluster_center(j,v) =0 continue; else cluster_center(j,v) = sum_wx/sum_w; end end end % computer clustering metric and % assign it as objective function value Fi of solution ,Si F = 0; for j= 1:K for ii = 1:N Temp=0; if solution_string(i,ii)==j; %%%%%%%%%%%%% 1--i 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; %%%%%%%%%%%%% 1--i end %for i = 1 : R % select best L solutions out of R solutions using objective function values % LOCAL SEARCH PROCEDURES==================================================== % fitness sort in ascending order [fitness_ascend,solution_index] = sort(solution_string(:,end),1); solution_ascend = [solution_string(solution_index,1:end-1) fitness_ascend]; % pls is local search threshold probability pls = 0.05; % perform local search on L solution L = 2; % local search procedures solution_temp = zeros(L,N+1); k = 1; while(k <= L) solution_temp(k,:) = solution_ascend(k,:); rp = rand(1,N); for i = 1:N if rp(i) <= pls current_cluster_number = setdiff([1:K],solution_temp(k,i)); change_cluster = randint(1,1,current_cluster_number); solution_temp(k,i) = change_cluster; end end % computer weights of all test samples and cluster centers solution_temp_weight = zeros(N,K); for h = 1:N solution_temp_cluster_index = solution_temp(k,h); %%%%%%%%%%%%% 1--k 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 % computer solution temp clustering metric and % assign it as objective function value Ft of solution ,St solution_temp_F = 0; for j= 1:K for ii = 1:N st_Temp=0; if solution_temp(k,ii)==j; %%%%%%%%%%%%% 1--k for v = 1:n