【摘要】 为了克服标准粒子群算法容易陷入局部最优的缺点,结合量子优化和反向学习的思想,提出一种混合反向学习和量子优化的粒子群算法.该混合算法在种群初始化、种群的跳越和种群最优个体的局部改进三方面上提高了量子粒子群算法的性能,有效地避免粒子群陷入局部最优并加速种群收敛.数值实验表明,混合算法在不同的函数优化方面具有较高的性能.更多还原

【Abstract】 In order to overcome the drawback of standard particle swarm algorithm which is easy to fall into local optimum,an improved particle swarm optimization algorithm is proposed combined with quantum optimization and opposition-based learning.There are three aspects that improve the quantum particle swarm algorithm performance: the initialization of population,population jumps and the best individual in the population of the local improvement.The improved algorithm can effectively avoid particle swarm into local optimum and accelerated population to the optimal position of the convergence.The numerical experiments show that the hybrid algorithm has high performance in different function optimization更多还原