用matlab写一个灰狼算法的伪代码和代码

以下是灰狼算法的伪代码：

初始化种群
计算适应度
while (满足终止条件) do
for each 灰狼 do
计算适应度
寻找领袖狼
更新位置
end for
更新参数
end while

以下是使用MATLAB实现灰狼算法的代码：

% 灰狼算法函数
function [best_pos, best_val] = grey_wolf_algorithm(obj_func, dim, lb, ub, max_iter, search_agent_num)
% 初始化参数
a = 2; % alpha常数
b = 1; % beta常数
d = 2; % delta常数
% 初始化种群
X = zeros(search_agent_num, dim);
for i = 1:search_agent_num
X(i,:) = lb + (ub-lb).*rand(1, dim); % 随机初始化位置
end
% 计算适应度
fitness = zeros(search_agent_num, 1);
for i = 1:search_agent_num
fitness(i) = obj_func(X(i,:));
end
% 初始化最优解
[global_best_val, global_best_index] = min(fitness);
global_best_pos = X(global_best_index,:);
% 迭代寻优
for iter = 1:max_iter
for i = 1:search_agent_num
% 计算适应度
fitness(i) = obj_func(X(i,:));
% 寻找领袖狼
if fitness(i) < global_best_val
global_best_val = fitness(i);
global_best_pos = X(i,:);
end
end
% 计算每个狼距离领袖狼的距离和排名
distance_to_alpha = zeros(search_agent_num, 1);
distance_to_beta = zeros(search_agent_num, 1);
distance_to_delta = zeros(search_agent_num, 1);
rank = zeros(search_agent_num, 1);
for i = 1:search_agent_num
distance_to_alpha(i) = norm(X(i,:) - global_best_pos);
distance_to_beta(i) = norm(X(i,:) - global_best_pos);
distance_to_delta(i) = norm(X(i,:) - global_best_pos);
rank(i) = fitness(i);
end
[~, sorted_index] = sort(rank);
alpha_index = sorted_index(1);
beta_index = sorted_index(2);
delta_index = sorted_index(3);
% 更新位置
for i = 1:search_agent_num
r1 = rand(1);
r2 = rand(1);
A1 = 2*a*r1 - a;
C1 = 2*r2;
D_alpha = abs(C1*global_best_pos - X(i,:));
X1 = global_best_pos - A1*D_alpha;
r1 = rand(1);
r2 = rand(1);
A2 = 2*a*r1 - a;
C2 = 2*r2;
D_beta = abs(C2*X(alpha_index,:) - X(i,:));
X2 = X(alpha_index,:) - A2*D_beta;
r1 = rand(1);
r2 = rand(1);
A3 = 2*a*r1 - a;
C3 = 2*r2;
D_delta = abs(C3*X(beta_index,:) - X(i,:));
X3 = X(beta_index,:) - A3*D_delta;
X(i,:) = (X1 + X2 + X3) / 3;
end
% 更新参数
a = 2 - iter * (2 / max_iter);
for i = 1:search_agent_num
if rand(1) < 0.5
b = 1;
X(i,:) = global_best_pos - b*abs(ub-lb)*rand(1,dim);
else
b = 2;
X(i,:) = global_best_pos - b*abs(ub-lb)*rand(1,dim);
end
if rand(1) < 0.5
d = 2;
X(i,:) = X(alpha_index,:) - d*abs(ub-lb)*rand(1,dim);
else
d = 1;
X(i,:) = X(alpha_index,:) - d*abs(ub-lb)*rand(1,dim);
end
end
end
% 返回最优解
best_pos = global_best_pos;
best_val = global_best_val;
end

% 测试函数
function y = test_func(x)
y = sum(x.^2);
end

% 测试例子
dim = 10; % 变量维度
lb = -100; % 变量下界
ub = 100; % 变量上界
max_iter = 100; % 最大迭代次数
search_agent_num = 20; % 搜索代数
[best_pos, best_val] = grey_wolf_algorithm(@test_func, dim, lb, ub, max_iter, search_agent_num);
disp(['最优解：',num2str(best_pos)]);
disp(['最优值：',num2str(best_val)]);

注意：以上代码只实现了灰狼算法的基本功能，可能需要根据具体问题进行修改。