灰狼优化算法matlab程序

下面是一个简单的灰狼优化算法的MATLAB程序示例：

% 灰狼优化算法 MATLAB程序示例

% 初始化种群大小和迭代次数
population_size = 50;
max_iterations = 100;

% 初始化问题维度和搜索空间范围
problem_dimension = 3;
lower_bound = [-10, -10, -10];
upper_bound = [10, 10, 10];

% 初始化灰狼群体
population = repmat(lower_bound, population_size, 1) + ...
    rand(population_size, problem_dimension) .* (repmat(upper_bound - lower_bound, population_size, 1));

% 初始化每只灰狼的适应度
fitness = zeros(population_size, 1);

% 开始迭代
for iteration = 1:max_iterations
    % 计算每只灰狼的适应度
    for i = 1:population_size
        fitness(i) = objective_function(population(i, :));
    end
    
    % 找到适应度最佳的灰狼和最差的灰狼
    [~, alpha_index] = min(fitness);
    [~, omega_index] = max(fitness);
    
    % 更新每只灰狼的位置
    for i = 1:population_size
        a = 2 * (1 - iteration / max_iterations);  % 计算线性递减的系数
        
        % 更新位置
        x_rand = population(randi(population_size), :);
        D_alpha = abs(a * rand * population(alpha_index, :) - population(i, :));
        D_omega = abs(a * rand * population(omega_index, :) - population(i, :));
        population(i, :) = x_rand - a * D_alpha + a * D_omega;
        
        % 限制位置在搜索空间的范围内
        population(i, :) = max(population(i, :), lower_bound);
        population(i, :) = min(population(i, :), upper_bound);
    end
end

% 打印最优解
[~, best_index] = min(fitness);
best_solution = population(best_index, :);
best_fitness = fitness(best_index);

disp('最优解:');
disp(best_solution);
disp('最优适应度:');
disp(best_fitness);

% 定义目标函数
function f = objective_function(x)
    % 这里可以根据问题定义自行编写目标函数
    % 示例：f = sum(x.^2);
end

请注意，这只是一个简单的示例程序，你需要根据你的具体问题和目标函数进行相应的修改。