matlab鲸鱼算法测试函数

鲸鱼算法（Whale Optimization Algorithm, WOA）是一种基于自然界鲸鱼捕食行为的优化算法。在MATLAB中，可以使用以下步骤编写一个简单的鲸鱼算法测试函数：

1. 首先，定义目标函数。这是您希望优化的函数。例如，我们可以使用Rosenbrock函数作为示例：

function y = rosenbrock(x)

    y = 100 * (x(2) - x(1)^2)^2 + (1 - x(1))^2;
end

2. 然后，编写鲸鱼算法的主要部分。这包括初始化鲸鱼群体、计算适应度、更新鲸鱼位置和选择新的鲸鱼位置等步骤。以下是一个简单的鲸鱼算法实现：

function [best_whale, best_fitness] = whale_optimization()

    % 初始化参数
    population_size = 50; % 鲸鱼数量
    generations = 100; % 迭代次数
    max_iterations = 10^6; % 最大迭代次数
    search_space = [-10, 10]; % 搜索空间范围
    alpha = 0.5; % 学习因子
    beta = 0.9; % 加速因子
    gamma = 1.4; % 惯性权重
    rho = 0.95; % 捕食系数
    theta = 2 * pi / generations; % 捕食周期

    % 初始化鲸鱼位置和适应度值
    whales = init_whales(population_size, search_space);    fitness = zeros(population_size, 1);
    for i = 1:population_size
        fitness(i) = rosenbrock(whales(i, :)');
    end
    best_fitness = min(fitness);
    best_whale = whales(min(fitness), :);
    old_fitness = best_fitness;
    old_whale = best_whale;

    % 迭代优化过程
    for t = 1:generations
        a = alpha * old_fitness + beta * abs(rand() - old_fitness); % 线性递减加速度项
        r = randn(); % 随机扰动项
        A = 2 * a * r - a; % A矩阵（捕食操作）
        C = randn(); % C向量（捕食操作）
        new_whales = update_whales(whales, A, C, search_space, max_iterations);
        new_fitness = evaluate_fitness(new_whales, rosenbrock);
        improved = new_fitness < old_fitness; % 如果适应度提高，则进行更新操作
        if improved || (t > theta && abs(new_fitness - old_fitness) < abs(old_fitness - best_fitness)) % 如果满足终止条件，则输出结果并返回最优解
            old_fitness = new_fitness;
            old_whale = new_whales(find(new_fitness < best_fitness, 1), :);
            if new_fitness < best_fitness
                best_fitness = new_fitness;
                best_whale = new_whales(find(new_fitness < best_fitness, 1), :);
            end
        end
        whales = new_whales; % 更新鲸鱼群体状态