上周忙着看公钥算法，基础要补的太多，看得晕晕的～～周末去了一趟河北，和博士一起去的，看到久违的“鸭子”，呵呵，大家改变都不大，鸭子现在做sp，我正好感兴趣，但他做的是固话的sp，主要是语音服务方面，呵呵，话题扯远了，于是我就没时间看我的ACO了。不知怎么的，我按照“晃晃悠悠”的提示进行改动，但我的fitness值始终没有收敛，用uci的iris的dataset，效果不好，准确率也不高，fitness值达到了280多，不知道咋回事。干脆把算法放到这里，各位有兴趣的朋友一起讨论一下，但要申明这是转自晃晃悠悠的blog：
% 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
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; %%%%%%%%%%%%% 1–k
% if Ft<Fl then Fl=Ft and Sl=St
if solution_temp(k,end) <= solution_ascend(k,end) %%%%%%%%%%%%% 1–k
solution_ascend(k,:) = solution_temp(k,:); %%%%%%%%%%%%% 1–k
end
if solution_ascend(k,end)<=best_solution_function_value
best_solution = solution_ascend(k,:);
end
k = k+1;
end %end while(local search)
% UPDATE PHEROMONE TRAIL MATRIX=============================================
% Update pheromone trail matrix using best L solution
% for i = 1 : N
% %for j = 1 : L
% tau(i,best_solution(1,i)) = (1 – rho) * tau(i,best_solution(1,i)) + rho / (sum(best_solution(1,end)));
% %end
% end
%
% t = t+1;
% end %while(main)
%++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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;
%use 1/tau_F or rho/tau_F, neither of them is good
end
%++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
t=t+1;
end
clc;
t
best_solution = solution_ascend(1,1:end-1)
best_solution_function_value = solution_ascend(1,end)
