matlab制作案例(2)

iter_max = 200; % 最大迭代次数 Route_best = zeros(iter_max,n); % 各代最佳路径 Length_best = zeros(iter_max,1); % 各代最佳路径的长度 Length_ave = zeros(iter_max,1); % 各代路径的平均长度 迭代寻找最佳路径 while iter <= iter_max

% 随机产生各个蚂蚁的起点城市 start = zeros(m,1); for i = 1:m

temp = randperm(n); start(i) = temp(1); end

Table(:,1) = start; % 构建解空间 citys_index = 1:n; % 逐个蚂蚁路径选择 for i = 1:m

% 逐个城市路径选择 for j = 2:n

tabu = Table(i,1:(j - 1)); % 已访问的城市集合(禁忌表) allow_index = ~ismember(citys_index,tabu);

allow = citys_index(allow_index); % 待访问的城市集合 P = allow;

% 计算城市间转移概率 for k = 1:length(allow)

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

P = P/sum(P);

% 轮盘赌法选择下一个访问城市 Pc = cumsum(P);

target_index = find(Pc >= rand); target = allow(target_index(1)); Table(i,j) = target; end end

% 计算各个蚂蚁的路径距离

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

% 计算最短路径距离及平均距离 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

% 更新信息素

Delta_Tau = zeros(n,n); % 逐个蚂蚁计算 for i = 1:m % 逐个城市计算 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; % 迭代次数加1，清空路径记录表

iter = iter + 1; Table = zeros(m,n); end 结果显示

[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)])]); 最短距离:15601.9195

最短路径:14 12 13 11 23 16 5 6 7 2 4 8 9 10 3 18 17 19 24 25 20 21 22 26 28 27 30 31 29 1 15 14 绘图 figure(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(['蚁群算法优化路径(最短距离:' num2str(Shortest_Length) ')']) figure(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('各代最短距离与平均距离对比')