螞蟻演算法matlab實現TSP

clear;

close all;

clc;

n = 35; %城市的數量

x = rand(1,n)*10;

y = rand(1,n)*10;

d = zeros(n,n);

for i = 1:n

for j = 1:n

if i~= j

d(i,j) = sqrt(power(x(i)-x(j),2) + power(y(i)-y(j),2));

else

d(i,j) = eps;

endend

endm = 80; %螞蟻的數量

alpha = 2;

beta = 6;

rho = 0.2; %揮發素

q = 10;

heu_f = 1./d; %先驗效應

tau = ones(n,n); % 初始化邊的資訊素

table = zeros(m,n);

iter = 1;

iter_max = 500;

route_best = zeros(iter_max,n); %最優路徑

length_best = zeros(iter_max,1); % 最優路徑的長度

%% 迭代

while iter <= iter_max

%% 初始化螞蟻出發點

start = zeros(m,1);

for i = 1:m

temp = randperm(n);

start(i) = temp(1);

end

%% 更新螞蟻的運動軌跡

table(:,1) = start;
city_index = 1:n;
for i = 1:m
for j = 2:n
has_visited = table(i,1:(j-1));
allow_index = ~ismember(city_index,has_visited);
allow = city_index(allow_index);
%%計算螞蟻轉移概率
p = allow;
for k = 1:length(allow)
p(k) = tau(has_visited(end),allow(k))^alpha *  heu_f(has_visited(end),allow(k))^ beta;
endp = p / sum(p);
pc = cumsum(p); % 輪盤,cumsum是依次求行和
target_index = find(pc >= rand);
target = allow(target_index(1));
table(i,j) = target;
endend%% 計算長度
length = zeros(m,1);
for i  = 1:m
route = table(i,:);
for j = 1:n-1
length(i) = length(i) + d(route(j),route(j+1));
endlength(i) = length(i) + d(route(n),route(1));
end%% 得到最佳路徑和長度
[min_length,min_index] = min(length);
if iter >1
if min_length > length_best(iter-1)
length_best(iter) = length_best(iter-1);
route_best(iter,:) = route_best(iter-1,:);
else
length_best(iter) = min_length;
route_best(iter,:) = table(min_index,:);
endelse 
length_best(iter) = min_length;
route_best(iter,:) = table(min_index,:);
end%%更新代數和資訊素
iter = iter+1;
delta_tau = zeros(n,n);
for i = 1:m
for j =1:n-1
delta_tau(table(i,j),table(i,j+1)) = delta_tau(table(i,j),table(i,j+1)) + q/length(i);
endendtau = (1-rho)*tau + delta_tau;
table = zeros(m,n);

plot(length_best);

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