给我一个matlab灰狼算法代码

下面是一个简单的 Matlab 灰狼算法实现代码，其中包含了灰狼算法的基本框架和参数设置：

% 灰狼算法求解函数最小值
% f: 待求解的函数句柄
% dim: 待求解的变量个数
% lb: 变量的下界
% ub: 变量的上界
% max_iter: 最大迭代次数
function [best_x, best_fval] = grey_wolf_optimizer(f, dim, lb, ub, max_iter)
    % 初始化灰狼群体
    population_size = 30;
    alpha_pos = zeros(1, dim);
    alpha_score = Inf;
    beta_pos = zeros(1, dim);
    beta_score = Inf;
    delta_pos = zeros(1, dim);
    delta_score = Inf;
    positions = repmat(lb, population_size, 1) + rand(population_size, dim) .* repmat(ub - lb, population_size, 1);
    
    % 开始迭代
    for iter = 1:max_iter
        % 计算适应度值
        scores = arrayfun(f, positions);
        
        % 更新 alpha, beta, delta
        [min_score, min_index] = min(scores);
        if min_score < alpha_score
            delta_score = beta_score;
            delta_pos = beta_pos;
            beta_score = alpha_score;
            beta_pos = alpha_pos;
            alpha_score = min_score;
            alpha_pos = positions(min_index, :);
        elseif min_score < beta_score
            delta_score = beta_score;
            delta_pos = beta_pos;
            beta_score = min_score;
            beta_pos = positions(min_index, :);
        elseif min_score < delta_score
            delta_score = min_score;
            delta_pos = positions(min_index, :);
        end
        
        % 计算新的位置
        a = 2 - iter * (2 / max_iter); % 调整 a 值
        for i = 1:population_size
            r1 = rand();
            r2 = rand();
            A1 = 2 * a * r1 - a;
            C1 = 2 * r2;
            D_alpha = abs(C1 * alpha_pos - positions(i, :));
            X1 = alpha_pos - A1 * D_alpha;
            
            r1 = rand();
            r2 = rand();
            A2 = 2 * a * r1 - a;
            C2 = 2 * r2;
            D_beta = abs(C2 * beta_pos - positions(i, :));
            X2 = beta_pos - A2 * D_beta;
            
            r1 = rand();
            r2 = rand();
            A3 = 2 * a * r1 - a;
            C3 = 2 * r2;
            D_delta = abs(C3 * delta_pos - positions(i, :));
            X3 = delta_pos - A3 * D_delta;
            
            positions(i, :) = (X1 + X2 + X3) / 3;
        end
    end
    
    % 返回最优解
    [best_fval, best_index] = min(scores);
    best_x = positions(best_index, :);
end

使用方法：

1. 定义待求解的函数句柄 `f`。
2. 定义变量的个数 `dim`、下界 `lb` 和上界 `ub`。
3. 调用 `grey_wolf_optimizer` 函数进行求解，返回最优解 `best_x` 和最优函数值 `best_fval`。

示例代码：

% 定义函数
f = @(x) sum(x.^2);
% 设置参数
dim = 10;
lb = -10 * ones(1, dim);
ub = 10 * ones(1, dim);
max_iter = 100;
% 求解最优解
[best_x, best_fval] = grey_wolf_optimizer(f, dim, lb, ub, max_iter);
% 输出结果
disp(['Best solution: ', num2str(best_x)]);
disp(['Best fitness: ', num2str(best_fval)]);