Matlab 模拟退火算法模型代码

梦里梦外; 2023-08-17 16:12 132阅读 0赞

![o\_%E7%A8%BF%E5%AE%9A%E8%AE%BE%E8%AE%A1%E5%AF%BC%E5%87%BA-20190912-220526.png][o_E7_A8_BF_E5_AE_9A_E8_AE_BE_E8_AE_A1_E5_AF_BC_E5_87_BA-20190912-220526.png]

function [best_solution,best_fit,iter] = mySa(solution,a,t0,tf,Markov)
    % 模拟退化算法
    % ===== 输入 ======%
    % solution 初始解 
    % a 温度衰减系数 0.99
    % t0 初始温度 120
    % tf 最终温度 1
    % Markov 马尔科夫链长度 10000
    % ====== 输出 =====%
    % best_solution 最优解
    % best_fit 最优解目标值
    % iter 迭代次数
    n = length(solution);
    t = t0;
    solution_new = solution;  % 初始解赋给最新的解
    best_fit = Inf;   % 初始化最优适应度（最差的适应度）
    fit = Inf;      %  初始化当前的适应度
    best_solution = solution;  % 最优解
    iter = 1;
    % -----------------------迭代过程------------------------------------%
    while t >= tf
        for j = 1:Markov
    % -----------------------产生新解过程------------------------------------%
            %进行扰动,产生新的序列solution_new;
            if (rand < 0.7) % 概率小于0.7 采取交换两个数位置的方式产生新解
                ind1 = 0;  ind2 = 0;
                while(ind1 == ind2 && ind1 >= ind2)
                    ind1 = ceil(rand*n);
                    ind2 = ceil(rand*n);
                end
                temp = solution_new(ind1);
                solution_new(ind1) = solution_new(ind2);
                solution_new(ind2) = temp;
            else % 概率大于等于0.7 采取成组交换连续三个数位置的方式产生新解
                ind = zeros(3,1);
                L_ind = length(unique(ind));
                while (L_ind < 3)
                    ind = ceil([rand*n rand*n rand*n]);
                    L_ind = length(unique(ind));
                end
                ind0 = sort(ind);
                a1 = ind0(1);  b1 = ind0(2);  c1 = ind0(3);
                solution0 = solution_new;
                solution0(a1:a1+c1-b1-1) = solution_new(b1+1:c1);
                solution0(a1+c1-b1:c1) = solution_new(a1:b1);
                solution_new = solution0;
            end
    % -----------------------计算适应度过程------------------------------------ %
            %计算适应度fit_new
            fit_new = myFitCal(solution_new);
    % -----------------------解的更新过程------------------------------------ %
            if fit_new < fit 
                fit = fit_new;
                solution = solution_new;
                %对最优路线和距离更新
                if       fit_new < best_fit
                    iter = iter + 1;
                    best_fit = fit_new;
                    best_solution = solution_new;
                end
            else
                if rand < exp(-(fit_new-fit)/t)
                    solution = solution_new;
                    fit = fit_new;
                end
            end
            solution_new = solution;
        end
        t = t*a; %降温
    end

转载于:https://www.cnblogs.com/gshang/p/11515394.html

[o_E7_A8_BF_E5_AE_9A_E8_AE_BE_E8_AE_A1_E5_AF_BC_E5_87_BA-20190912-220526.png]: /images/20230810/e75501ff3a684fa88cefbd51dab2eff2.png