基于MATLAB的变色龙优化算法代码

以下是基于MATLAB的变色龙优化算法代码：

% 变色龙优化算法
function BOA()

% 初始化
popsize = 50;    % 种群大小
maxgen = 100;    % 最大迭代次数
dim = 30;        % 变量维度
lb = -100;       % 变量下界
ub = 100;        % 变量上界
alpha = 0.5;     % 变异因子
beta = 0.5;      % 向量相减系数
gamma = 0.1;     % 步长系数
delta = 0.1;     % 随机扰动系数

% 初始化种群
pop = rand(popsize, dim) * (ub - lb) + lb;
fitness = objfun(pop);

% 迭代
for i = 1:maxgen
    
    % 变异
    for j = 1:popsize
        idx = randperm(popsize, 3);
        v = pop(idx(1), :) + alpha * (pop(idx(2), :) - pop(idx(3), :));
        v = max(v, lb);
        v = min(v, ub);
        
        % 选择
        if objfun(v) < fitness(j)
            pop(j, :) = v;
            fitness(j) = objfun(v);
        end
    end
    
    % 聚合
    for j = 1:popsize
        for k = 1:dim
            p = pop(j, :);
            delta_p = delta * randn(1, dim);
            q = p + delta_p;
            q = max(q, lb);
            q = min(q, ub);
            if objfun(q) < objfun(p)
                p(k) = q(k);
                pop(j, :) = p;
                fitness(j) = objfun(p);
            else
                d = beta * randn(1, dim) .* (p - pop(randperm(popsize, 1), :));
                q = p + gamma * d;
                q = max(q, lb);
                q = min(q, ub);
                if objfun(q) < objfun(p)
                    p(k) = q(k);
                    pop(j, :) = p;
                    fitness(j) = objfun(p);
                end
            end
        end
    end
    
    % 输出结果
    disp(['Iteration: ', num2str(i), ', Best fitness: ', num2str(min(fitness))]);
end

end

% 目标函数
function f = objfun(x)
f = sum(x .^ 2);
end

这段代码实现了变色龙优化算法，并使用了一个简单的目标函数作为演示。要使用该代码，只需将`objfun`函数替换为需要优化的目标函数即可。