Matlab 模拟退火算法模型代码

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(randn);

ind2 = ceil(randn);

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([randn randn 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