梯度金字塔机制（GPM）元启发式优化算法matlab代码.zip

function [best_solution, best_fitness] = gradient_pyramid_mechanism(obj_func, num_variables, num_agents, max_iter, num_levels, lb, ub) % Initialize population at the coarsest level population = bsxfun(@plus, lb, bsxfun(@times, rand(num_agents, num_variables), (ub - lb))); fitness = zeros(1, num_agents); % Evaluate fitness of each agent at the coarsest level for i = 1:num_agents fitness(i) = obj_func(population(i, :)); end % Main loop for iter = 1:max_iter % Divide the solution space into levels for level = 1:num_levels % Refine the solutions at the current level % (e.g., through local search or perturbation) % Update position and fitness of each agent at the current level for i = 1:num_agents % Generate a new solution by perturbing the current one new_solution = population(i, :) + randn(1, num_variables); % Random perturbation using normal distribution % Clip new solution to ensure it stays within bounds new_solution = max(min(new_solution, ub), lb); % Evaluate fitness of the new solution new_fitness = obj_func(new_solution); % Update if the new solution is better if new_fitness < fitness(i) population(i, :) = new_solution; fitness(i) = new_fitness; end end end end % Find the best solution in the final population [best_fitness, best_index] = min(fitness); best_solution = population(best_index, :); end