Nelder-Mead 单纯形增强群体捕食算法matlab代码.zip

function [best_solution, best_fitness] = nelder_mead_cpa(obj_func, num_variables, num_agents, max_iter, lb, ub) % Initialize population population = bsxfun(@plus, lb, bsxfun(@times, rand(num_agents, num_variables), (ub - lb))); fitness = zeros(1, num_agents); % Evaluate fitness of each agent for i = 1:num_agents fitness(i) = obj_func(population(i, :)); end % Main loop for iter = 1:max_iter % Perform Colony Predation Algorithm (CPA) % Explore the search space using CPA % Perform local optimization using Nelder-Mead simplex method for i = 1:num_agents % Perform Nelder-Mead simplex method to improve the solution [population(i, :), fitness(i)] = fminsearch(@(x) obj_func(x), population(i, :)); end % Update population based on CPA and Nelder-Mead results % (Optional) Apply elitism or other strategies to select the best solutions end % Find the best solution in the final population [best_fitness, best_index] = min(fitness); best_solution = population(best_index, :); end