摘要
为改善基本粒子群的全局、局部搜索能力和收敛速度以及计算精度,基于经典PSO方法和量子理论基础之上,提出了一种改进的基于量子行为的PSO算法--cQPSO算法。新算法中,采用全同粒子系更新粒子位置,并引用混沌思想,对每个粒子进行混沌搜索,试图改善粒子的全局、局部搜索能力和收敛速度以及计算精度。对经典函数的测试计算表明,改进算法的性能优于经典的PSO算法、基于量子行为的PSO算法。
To improve full searching ability, local searching ability, convergence rate and calculating precision of elementary particle swarm, based on classical PSO algorithm and quanta theory, an improved PSO algorithm with quantum behavior--cQPSO algorithm is proposed. Identical particle system is introduced to update the position of particle and chaos thought is introduced to chaotic search every particle, accordingly improving the full searching ability, local searching ability, convergence rate and calculating precision of elementary particle swarm. The experimental results of classical function show that capability of improved algorithm is superior to classical PSO algorithm and PSO algorithm with quantum behavior.
出处
《计算机工程与设计》
CSCD
北大核心
2009年第17期4074-4076,共3页
Computer Engineering and Design
基金
中北大学校级基金项目(2007-12-26)
关键词
粒子群优化算法
量子行为
混沌思想
局部搜索
全同粒子系
particle swarm optimization algorithm （PSO）
quantum behavior
chaos thought
local searching
identical particle system
