融合混沌算子和反向学习的差分进化算法matlab

以下是一个简单的 MATLAB 代码示例，演示了如何实现融合混沌算子和反向学习的差分进化算法 RL-CODE：

function [best_solution, best_fitness] = RL_CODE(fitness_func, dim, lb, ub, max_iter)
% fitness_func: 适应度函数句柄
% dim: 问题维度
% lb: 下限
% ub: 上限
% max_iter: 最大迭代次数

% 初始化算法参数
CR = 0.5;  % 交叉概率
F = 0.5;   % 缩放因子
NP = 50;   % 种群规模
record = zeros(max_iter, 1);  % 记录每一代的最优适应度值

% 初始化种群
pop = lb + (ub - lb) * rand(NP, dim);
fitness = feval(fitness_func, pop);
best_solution = pop(1, :);
best_fitness = fitness(1);

% 初始化混沌序列
x = rand(1);
y = rand(1);
z = rand(1);
for i = 1 : 1000
    x_next = y - z;
    y_next = x + 0.2 * y;
    z_next = 0.2 + z * (x - 10);
    x = x_next;
    y = y_next;
    z = z_next;
end

% 迭代优化
for iter = 1 : max_iter
    % 记录每一代的最优适应度值
    record(iter) = best_fitness;

    % 更新控制参数
    if iter > 1
        CR = CR + 0.02 * (rand(1) - 0.5);
        F = F + 0.02 * (rand(1) - 0.5);
        CR = max(0, min(CR, 1));
        F = max(0, min(F, 1));
    end

    % 反向学习机制
    if iter > 20 && mod(iter, 10) == 0
        [idx, sorted_fitness] = sort(fitness, 'ascend');
        pop = pop(sorted_fitness, :);
        fitness = fitness(sorted_fitness);
        for i = 1 : NP
            j = randi([1 NP]);
            while j == i
                j = randi([1 NP]);
            end
            if fitness(i) < fitness(j)
                pop(i, :) = pop(i, :) + rand(1, dim) .* (pop(j, :) - pop(i, :));
            end
        end
        fitness = feval(fitness_func, pop);
        [best_fitness, best_idx] = min(fitness);
        best_solution = pop(best_idx, :);
    end

    % 差分进化算法
    for i = 1 : NP
        % 选择3个不同的个体
        idxs = randperm(NP, 3);
        while idxs(1) == i
            idxs = randperm(NP, 3);
        end

        % 生成新个体
        v = pop(idxs(1), :) + F * (pop(idxs(2), :) - pop(idxs(3), :));
        v = max(v, lb);
        v = min(v, ub);

        % 混沌序列变换
        x = mod(x * 2^32, 1);
        y = mod(y * 2^32, 1);
        z = mod(z * 2^32, 1);
        r = mod(x + y + z, 1);
        if rand(1) < r
            j = randi([1 dim]);
            v(j) = v(j) + randn(1);
        end

        % 交叉变异
        idx = randi([1 dim]);
        for j = 1 : dim
            if rand(1) < CR || j == idx
                pop_new(i, j) = v(j);
            else
                pop_new(i, j) = pop(i, j);
            end
        end
    end

    % 更新种群
    fitness_new = feval(fitness_func, pop_new);
    for i = 1 : NP
        if fitness_new(i) < fitness(i)
            pop(i, :) = pop_new(i, :);
            fitness(i) = fitness_new(i);
        end
        if fitness(i) < best_fitness
            best_fitness = fitness(i);
            best_solution = pop(i, :);
        end
    end
end

% 返回最优解和最优适应度值
end

需要注意的是，上述代码仅为示例，实际应用中可能需要对算法参数进行更细致的调整，以获得更好的优化效果。