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.
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
出处
《微电子学与计算机》
CSCD
北大核心
2013年第6期126-130,共5页
Microelectronics & Computer
基金
国家自然科学基金项目(70701013)
河南省教育厅2007年自然科学研究计划(2007520068)
关键词
粒子群优化
量子优化
反向学习
函数优化
particle swarm optimizatiom quantum optimization
opposition-based learning
function optimization
