matlab 模拟退火代码模板

以下是一个简单的matlab模拟退火代码模板：

function [xmin, fmin] = sim_anneal(obj_fun, x0, options)
% Simulated Annealing Algorithm for Function Minimization
% obj_fun - function handle: objective function to be minimized
% x0 - vector: initial guess for minimum (1xn)
% options - structure: various parameters for the algorithm
%      options.t0 - initial temperature
%      options.alpha - cooling rate
%      options.stopping_temp - minimum temperature at which annealing stops
%      options.max_iter - maximum number of iterations
%      options.display - flag for displaying progress

% Initialize parameters
t = options.t0;
x = x0;
[fmin, xmin] = obj_fun(x);
i = 1;

while t > options.stopping_temp && i < options.max_iter
    % Generate new candidate solution
    xnew = x + randn(size(x)); % Gaussian perturbation
    
    % Evaluate objective at new candidate solution
    [fnew, ~] = obj_fun(xnew);
    
    % Determine whether to accept new solution
    dE = fnew - fmin; % Change in objective function
    if dE < 0 || rand() < exp(-dE/t) % Accept new solution
        x = xnew;
        fmin = fnew;
    end
    
    % Update temperature and iteration counter
    t = options.alpha * t;
    i = i + 1;
    
    % Display progress
    if options.display
        fprintf('Iteration %d: Temperature = %g, Objective = %g\n', i, t, fmin);
    end
end
end

这个函数接受一个指定目标函数的函数句柄、一个初始解向量和一个设置参数结构体。算法随后在一系列的迭代中搜索全局最小值，并返回最小解向量和最小函数值。你可以通过设定结构体的不同参数来调整算法的表现，例如初始温度、冷却速率、停止温度以及最大迭代次数等。