Matlab 实现：常青藤优化算法求解参数最优解的主函数代码剖析

e = 0.7; % 常青藤腐蚀率（移除个体比例） exploration_rate = 0.3; % 探索概率（选择非最优个体的概率） % 初始化常青藤种群 [ivy, q, p, error] = initialize_ivy(population_size, s_range, t_range, u_range); % 迭代优化 for iter = 1:max_iters % 评估适应度函数 fitness = evaluate_fitness(ha, hp, ea, ep, q, p, error); % 保存当前迭代中最优解 [best_fitness, best_index] = min(fitness); best_ivy = ivy(:, best_index); best_q = q(:, best_index); best_p = p(:, best_index); % 保存每次迭代中最优解 qsave(iter, 1) = best_q(1); qsave(iter, 2) = best_q(2); qsave(iter, 3) = best_q(3); % 输出当前最优解的适应度值 disp(['Iteration: ', num2str(iter), ' Best Fitness: ', num2str(best_fitness)]); % 常青藤生长和腐蚀 [new_ivy, new_q, new_p] = grow_and_decay_ivy(ivy, q, p, ivy_growth_rate, e); % 探索非最优解 explore_ivy = explore_ivy(ivy, exploration_rate); % 更新常青藤种群 ivy = [best_ivy, new_ivy, explore_ivy]; q = [best_q, new_q, zeros(size(explore_ivy, 2), 3)]; p = [best_p, new_p, zeros(size(explore_ivy, 2), 3)]; % 交叉和变异 ivy = crossover(ivy); ivy = mutation(ivy); end % 保存结果 save_result(qsave); end ######[AI写代码神器 | 930点数解答 | 2024-12-05 22:53:20]