遗传算法模拟退火matlab编程.doc

单钻头退火算法matlab编程clear clc a = 0.999; % 温度衰减函数的参数 t0 = 97; tf = 3; t = t0; Markov_length = 2800; % Markov链长度 coordinates = ; coordinates(:,1) = ; amount = size(coordinates,1); % 城市的数目 % 通过向量化的方法计算距离矩阵 dist_matrix = zeros(amount, amount); coor_x_tmp1 = coordinates(:,1) * ones(1,amount); coor_x_tmp2 = coor_x_tmp1; coor_y_tmp1 = coordinates(:,2) * ones(1,amount); coor_y_tmp2 = coor_y_tmp1; dist_matrix = sqrt(coor_x_tmp1-coor_x_tmp2).2 + . (coor_y_tmp1-coor_y_tmp2).2); sol_new = 1:amount; % 产生初始解% sol_new是每次产生的新解；sol_current是当前解；sol_best是冷却中的最好解； E_current = inf;E_best = inf; % E_current是当前解对应的回路距离；% E_new是新解的回路距离；% E_best是最优解的 sol_current = sol_new; sol_best = sol_new; p = 1; while t=tf for r=1:Markov_length % Markov链长度 % 产生随机扰动 if (rand 0.5) % 随机决定是进行两交换还是三交换 % 两交换 ind1 = 0; ind2 = 0; while (ind1 = ind2) ind1 = ceil(rand.*amount); ind2 = ceil(rand.*amount); end tmp1 = sol_new(ind1); sol_new(ind1) = sol_new(ind2); sol_new(ind2) = tmp1; else % 三交换 ind1 = 0; ind2 = 0; ind3 = 0; while (ind1 = ind2) | (ind1 = ind3) . | (ind2 = ind3) | (abs(ind1-ind2) = 1) ind1 = ceil(rand.*amount); ind2 = ceil(rand.*amount); ind3 = ceil(rand.*amount); end tmp1 = ind1;tmp2 = ind2;tmp3 = ind3; % 确保ind1 ind2 ind3 if (ind1 ind2) & (ind2 ind3) ; elseif (ind1 ind3) & (ind3 ind2) ind2 = tmp3;ind3 = tmp2; elseif (ind2 ind1) & (ind1 ind3) ind1 = tmp2;ind2 = tmp1; elseif (ind2 ind3) & (ind3 ind1) ind1 = tmp2;ind2 = tmp3; ind3 = tmp1; elseif (ind3 ind1) & (ind1 ind2) ind1 = tmp3;ind2 = tmp1; ind3 = tmp2; elseif (ind3 ind2) & (ind2 ind1) ind1 = tmp3;ind2 = tmp2; ind3 = tmp1; end tmplist1 = sol_new(ind1+1):(ind2-1); sol_new(ind1+1):(ind1+ind3-ind2+1) = . sol_new(ind2):(ind3); sol_new(ind1+ind3-ind2+2):ind3) = . tmplist1; end %检查是否满足约束 % 计算目标函数值（即内能） E_new = 0; for i = 1 : (amount-1) E_new = E_new + . dist_matrix(sol_new(i),sol_new(i+1); end % 再算上从最后一个城市到第一个城市的距离 %E_new = E_new + . %dist_matrix(sol_new(amount),sol_new(1); if E_new E_current E_current = E_new; sol_current = sol_new; if E_new E_best% 把冷却过程中最好的解保存下来 E_best = E_new; sol_best = sol_new; end else % 若新解的目标函数值小于当前解的， % 则仅以一定概率接受新解 if rand exp(-(E_new-E_current)./t) E_current = E_new; sol_current = sol_new; else sol_new = sol_current; end end end t=t.*a; % 控制参数t（温度）减少为原来的a倍 end disp(最优解为：) disp(sol_best) disp(最短距离：) disp(E_best) figure() set(gcf,Name,monituihuo-sol_best,Color,r) N=length(sol_best); scatter(coordinates(:,1),coordinates(:,2),50,filled); hold on plot(coordinates(sol_best(1),1),coordinates(sol_best(N),1),coordinates(sol_best(1),2),coordinates(sol_best(N),2) set(gca,Color,g) hold on for