一种改进的自适应逃逸微粒群算法及实验分析

	一种改进的自适应逃逸微粒群算法及实验分析
	赫然 ; 王永吉 ; 王青 ; 周津慧 ; 胡陈勇
刊名	软件学报
	2005
卷号	16 期号:12 页码:2036-2044
关键词	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
ISSN号	1000-9825
其他题名	an improved particle swarm optimization based on self-adaptive escape velocity
中文摘要	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．
收录类别	ei,cscd,wanfang,cnki
资助信息	China Computer Federation; IEEE Computer Society
语种	中文
公开日期	2010-08-17
附注	To deal with the problem of premature convergence and slow search speed, this paper proposes a novel particle swarm optimization （PSO） called self-adaptive escape PSO, which is guaranteed to converge to the global optimization solution with probability one. Considering that the organisms have the phenomena of escaping from the original cradle when they find the survival density is too high to live, this paper uses a special mutation -escape operator to make particles explore the search space more efficiently. The novel strategy produces a large speed value dynamically according to the variation of the speed, which makes the algorithm explore the local and global minima thoroughly at the same time. Experimental simulations show that the proposed method can not only significantly speed up the convergence, but also effectively solve the premature convergence problem.
内容类型	期刊论文
源URL	[http://124.16.136.157/handle/311060/3238]
专题	软件研究所_互联网软件技术实验室 _期刊论文
推荐引用方式 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.