摘要
通过分析粒子群优化算法的特点,将粒子放在量子空间来描述,建立粒子的量子势能场模型,并结合群体的群集性推导了量子粒子群优化(QPSO)算法.在随机算法全局收敛定理的框架下,讨论了QPSO算法的收敛性,证明QPSO算法是一种全局收敛的算法.针对QPSO算法的唯一控制参数,提出了三种控制策略,结合标准测试函数的仿真结果给出了具有实际指导意义的控制参数选择方法.
Based on the analysis of particle swarm optimization algorithm,the particle is described in the quantum space and the potential energy field model is created. And then according to the swarm＇s gregariousness,the quantum-behaved particle swarm optimization （ QPSO） algorithm is derived. Within the framework of random algorithm＇s global convergence theorem,the convergence of QPSO algorithm is discussed and is proved to be a kind of global convergence algorithm. Three kinds of control strategy are proposed for the unique parameter of QPSO algorithm and they are tested on five benchmark functions. According to the test results,some conclusions concerning the selection of the parameter are drawn.
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2010年第6期3686-3694,共9页
Acta Physica Sinica
基金
国家自然科学基金(批准号:60474030)
江南大学科研基金(批准号:1055210322090270
1055211542080210)资助的课题~~
关键词
量子系统
势阱
全局收敛
粒子群优化
quantum system
potential well
global convergence
particle swarm optimization
