基于matlab的粒子群算法 源码.7z

clc clear numItems = 10; weights = [2, 3, 4, 5, 9, 7, 8, 6, 4, 5]; values = [3, 4, 8, 8, 10, 7, 6, 5, 3, 7]; capacity = 20; popSize = 50; N_Generations = 500; cross_Rate = 0.8; mutation_Rate = 0.1; population = randi([0, 1], popSize, numItems); best_Fitness = zeros(N_Generations, 1); for gen = 1:N_Generations fitness = evaluate_Fitness(population, weights, values, capacity); best_Fitness(gen) = max(fitness); selected_Population = selection(population, fitness); new_Population = zeros(popSize, numItems); for i = 1:2:popSize if rand < cross_Rate parent1 = selected_Population(i, :); parent2 = selected_Population(i+1, :); [offspring1, offspring2] = Crossover(parent1, parent2); new_Population(i, :) = offspring1; new_Population(i+1, :) = offspring2; else new_Population(i, :) = selected_Population(i, :); new_Population(i+1, :) = selected_Population(i+1, :); end end for i = 1:popSize new_Population(i, :) = mutate(new_Population(i, :), mutation_Rate); end population = new_Population; end finalFitness = evaluate_Fitness(population, weights, values, capacity); [bestValue, bestIndex] = max(finalFitness); bestSolution = population(bestIndex, :);