Matlab 模擬退火算法模型代碼

function [best_solution,best_fit,iter] = mySa(solution,a,t0,tf,Markov)
% 模擬退化算法
% ===== 輸入 ======%
% solution 初始解 
% a 溫度衰減系數 0.99
% t0 初始溫度 120
% tf 最終溫度 1
% Markov 馬爾科夫鏈長度 10000
% ====== 輸出 =====%
% best_solution 最優解
% best_fit 最優解目標值
% iter 迭代次數
n = length(solution);
t = t0;
solution_new = solution;  % 初始解賦給最新的解
best_fit = Inf;   % 初始化最優適應度（最差的適應度）
fit = Inf;      %  初始化當前的適應度
best_solution = solution;  % 最優解
iter = 1;
% -----------------------迭代過程------------------------------------%
while t >= tf
    for j = 1:Markov
% -----------------------產生新解過程------------------------------------%
        %進行擾動,產生新的序列solution_new;
        if (rand < 0.7) % 概率小於0.7 采取交換兩個數位置的方式產生新解
            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 % 概率大於等於0.7 采取成組交換連續三個數位置的方式產生新解
            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
% -----------------------計算適應度過程------------------------------------ %
        %計算適應度fit_new
        fit_new = myFitCal(solution_new);
% -----------------------解的更新過程------------------------------------ %
        if fit_new<fit 
            fit = fit_new;
            solution = solution_new;
            %對最優路線和距離更新
            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; %降溫
end