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一种改进的自适应逃逸微粒群算法及实验分析
Alternative Titlean improved particle swarm optimization based on self-adaptive escape velocity
赫然; 王永吉; 王青; 周津慧; 胡陈勇
2005
Source软件学报
ISSN1000-9825
Volume16Issue:12Pages:2036-2044
English Abstract分析了变异操作对微粒群算法(panicle swarm optimization,简称PSO)的影响,针对收敛速度慢、容易陷入局部极小等缺点,结合生物界中物种发现生存密度过大时会自动分家迁移的习性,给出了一种自适应逃逸微粒群算法,并证明了它依概率收敛到全局最优解.算法中的逃逸行为是一种简化的确定变异操作.当微粒飞行速度过小时,通过逃逸运动使微粒能够有效地进行全局和局部搜索,减弱了随机变异操作带来的不稳定性、典型复杂函数优化的仿真结果表明,该算法不仅具有更快的收敛速度,而且能更有效地进行全局搜索.
Indexed Typeei,cscd,wanfang,cnki
Keyword微粒群算法 逃逸速度 自适应 变异操作 群体智能 Particle Swarm Optimization Escape Velocity Self-adaptive Mutation Swarm Intelligence
Department互联网软件技术实验室
SponsorshipChina Computer Federation; IEEE Computer Society
Language中文
Content Type期刊论文
URIhttp://ir.iscas.ac.cn/handle/311060/3238
Collection互联网软件技术实验室
Recommended Citation
GB/T 7714
赫然,王永吉,王青,等. 一种改进的自适应逃逸微粒群算法及实验分析[J]. 软件学报,2005,16(12):2036-2044.
APA 赫然,王永吉,王青,周津慧,&胡陈勇.(2005).一种改进的自适应逃逸微粒群算法及实验分析.软件学报,16(12),2036-2044.
MLA 赫然,et al."一种改进的自适应逃逸微粒群算法及实验分析".软件学报 16.12(2005):2036-2044.
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