给我混合单纯形差分进化算法的matlab代码

混合单纯形差分进化（Hybrid Simulated Annealing - Differential Evolution, HSADe）是一种改进的优化算法，结合了差分进化（Differential Evolution, DE）和模拟退火（Simulated Annealing, SA）的优点。以下是一个简化的HSADe算法的基本MATLAB代码示例：

function [solution, fitness] = hsa_de(objfun, lb, ub, pop_size, iter, crossover_rate, mutation_rate, cooling_rate)
% 初始化种群
pop = rand(pop_size, length(lb));
fitness = objfun(pop);

% 模拟退火参数
T = max(fitness);
alpha = 0.95; % 冷却率

for t = 1:iter
    % 差分进化部分
    for i = 1:pop_size
        % 随机选择三个个体
        idxs = randperm(pop_size, 3);
        a, b, c = pop(idxs(1)), pop(idxs(2)), pop(idxs(3));

        % 更新解
        mutant = a + mutation_rate * abs(b - c);
        if isinside(mutant, lb, ub) && objfun(mutant) < objfun(pop(i))
            pop(i) = mutant;
        end
    end

    % 混合模拟退火
    for i = 1:length(pop)
        % 计算邻居解
        neighbor = pop(i) + cooling_rate ./ (t+1) .* (randn(size(pop(i))) .* (ub - lb) + lb);

        % 检查是否接受邻居解
        if objfun(neighbor) <= objfun(pop(i)) || rand() < exp(-(objfun(neighbor) - objfun(pop(i)))) / T
            pop(i) = neighbor;
        end
        
        % 更新温度
        T *= alpha;
    end
    
    % 最佳解更新
    [solution, fitness] = minmax(pop, fitness);
end

% 辅助函数：找到最小值和最大值的索引
[min_idx, ~] = min(fitness);
[max_idx, ~] = max(fitness);
solution = pop(min_idx);
fitness = fitness(min_idx);
end

%