请用matlab写一个用于求解三变量函数最小值的遗传粒子群算法

由于您没有给出具体的三变量函数，以下代码仅提供一个基本的遗传粒子群算法框架，您可以根据自己的需求进行修改。

% 遗传粒子群算法求解三变量函数最小值

% 参数设置
pop_size = 50; % 粒子群大小
max_iter = 100; % 最大迭代次数
c1 = 2; % 加速常数1
c2 = 2; % 加速常数2
w = 0.8; % 惯性权重
pc = 0.8; % 交叉概率
pm = 0.1; % 变异概率

% 初始化种群
pop = rand(pop_size, 3);
vel = zeros(pop_size, 3);
pbest = pop;
gbest = pbest(1, :);

% 计算适应度
fit = zeros(pop_size, 1);
for i = 1:pop_size
fit(i) = your_function(pop(i, 1), pop(i, 2), pop(i, 3));
if fit(i) < your_function(gbest(1), gbest(2), gbest(3))
gbest = pop(i, :);
end
end

% 迭代更新
for iter = 1:max_iter
% 更新速度和位置
for i = 1:pop_size
vel(i, :) = w * vel(i, :) + c1 * rand(1, 3) .* (pbest(i, :) - pop(i, :)) + c2 * rand(1, 3) .* (gbest - pop(i, :));
pop(i, :) = pop(i, :) + vel(i, :);

% 边界处理
pop(i, pop(i, :) < 0) = 0;
pop(i, pop(i, :) > 1) = 1;
end

% 交叉和变异
for i = 1:pop_size
if rand < pc
j = randi([1, pop_size]);
k = randi([1, 3]);
pop(i, k) = (pop(i, k) + pop(j, k)) / 2;
end
if rand < pm
k = randi([1, 3]);
pop(i, k) = rand;
end
end

% 更新个体最优和全局最优
for i = 1:pop_size
new_fit = your_function(pop(i, 1), pop(i, 2), pop(i, 3));
if new_fit < fit(i)
fit(i) = new_fit;
pbest(i, :) = pop(i, :);
if fit(i) < your_function(gbest(1), gbest(2), gbest(3))
gbest = pop(i, :);
end
end
end

% 输出迭代结果
fprintf('Iteration %d: gbest = %f\n', iter, your_function(gbest(1), gbest(2), gbest(3)));
end

% 输出最终结果
fprintf('Final result: gbest = %f\n', your_function(gbest(1), gbest(2), gbest(3)));