load citys_data.mat  

n = size(citys,1);

D = zeros(n,n);

for i = 1:n

    for j = 1:n

        if i ~= j

            D(i,j) = sqrt(sum((citys(i,:) - citys(j,:)).^2));

        else

            D(i,j) = 1e-4;

        end

    end

end

m = 50;

alpha = 1;

beta = 5;

rho = 0.1;

Q = 1;

Eta = 1./D;

Tau = ones(n,n);

Table = zeros(m,n);

iter = 1;

iter_max = 200;

Route_best = zeros(iter_max,n);

Length_best = zeros(iter_max,1);

Length_ave = zeros(iter_max,1);      

while iter <= iter_max

      start = zeros(m,1);

      for i = 1:m

          temp = randperm(n);

          start(i) = temp(1);

      end

      Table(:,1) = start;

      citys_index = 1:n;

      for i = 1:m

         for j = 2:n

             tabu = Table(i,1:(j - 1));

             allow_index = ~ismember(citys_index,tabu);

             allow = citys_index(allow_index);

             P = allow;

             for k = 1:length(allow)

                 P(k) = Tau(tabu(end),allow(k))^alpha * Eta(tabu(end),allow(k))^beta;

             end

             P = P/sum(P);

             Pc = cumsum(P);

            target_index = find(Pc >= rand);

            target = allow(target_index(1));

            Table(i,j) = target;

         end

      end

      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));

          end

          Length(i) = Length(i) + D(Route(n),Route(1));

      end

      if iter == 1

          [min_Length,min_index] = min(Length);

          Length_best(iter) = min_Length;

          Length_ave(iter) = mean(Length);

          Route_best(iter,:) = Table(min_index,:);

      else

          [min_Length,min_index] = min(Length);

          Length_best(iter) = min(Length_best(iter - 1),min_Length);

          Length_ave(iter) = mean(Length);

          if Length_best(iter) == min_Length

              Route_best(iter,:) = Table(min_index,:);

          else

              Route_best(iter,:) = Route_best((iter-1),:);

          end

      end

      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);

          end

          Delta_Tau(Table(i,n),Table(i,1)) = Delta_Tau(Table(i,n),Table(i,1)) + Q/Length(i);

      end

      Tau = (1-rho) * Tau + Delta_Tau;

    iter = iter + 1;

    Table = zeros(m,n);

end

[Shortest_Length,index] = min(Length_best);

Shortest_Route = Route_best(index,:);

disp(['最短距离:' num2str(Shortest_Length)]);

disp(['最短路径:' num2str([Shortest_Route Shortest_Route(1)])]);

subplot(1,2,1);

plot([citys(Shortest_Route,1);citys(Shortest_Route(1),1)],...

     [citys(Shortest_Route,2);citys(Shortest_Route(1),2)],'o-');

grid on

for i = 1:size(citys,1)

    text(citys(i,1),citys(i,2),['   ' num2str(i)]);

end

text(citys(Shortest_Route(1),1),citys(Shortest_Route(1),2),'       起点');

text(citys(Shortest_Route(end),1),citys(Shortest_Route(end),2),'       终点');

xlabel('城市位置横坐标')

ylabel('城市位置纵坐标')

title(['ACA：利用ACA算法解决TSP优化路径(最短距离:' num2str(Shortest_Length) ')—Jason niu'])

subplot(1,2,2);

plot(1:iter_max,Length_best,'b',1:iter_max,Length_ave,'r:')

legend('最短距离','平均距离')

xlabel('迭代次数')

ylabel('距离')

title('ACA：各代最短距离与平均距离对比—Jason niu')