[1]孙文新,穆华平.自适应群体结构的粒子群优化算法[J].智能系统学报,2013,8(04):372-376.[doi:10.3969/j.issn.1673-4785.201211041]
 SUN Wenxin,MU Huaping.Particle swarm optimization based on self-adaptive population structure[J].CAAI Transactions on Intelligent Systems,2013,8(04):372-376.[doi:10.3969/j.issn.1673-4785.201211041]
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自适应群体结构的粒子群优化算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第8卷
期数:
2013年04期
页码:
372-376
栏目:
出版日期:
2013-08-25

文章信息/Info

Title:
Particle swarm optimization based on self-adaptive population structure
文章编号:
1673-4785(2013)04-0372-05
作者:
孙文新1穆华平2
1. 鹤壁职业技术学院 电子信息工程学院,河南 鹤壁 458030; 2. 鹤壁职业技术学院 公共基础教研部,河南 鹤壁 458030
Author(s):
SUN Wenxin1 MU Huaping2
1.School of Electronic Information Engineering, Hebi Occupation Technology College, Hebi 458030, China; 2. Department of Public Infrastructure, Hebi Occupation Technology College, Hebi 458030, China
关键词:
粒子群优化算法自适应群体结构惯性权重
Keywords:
particle swarm optimization self-adaptive population structure inertia weight
分类号:
TP18
DOI:
10.3969/j.issn.1673-4785.201211041
文献标志码:
A
摘要:
粒子群优化算法中,群体结构的组织模式直接决定了粒子间信息的共享和交流方式.根据复杂网络形成过程中的动力学原理,提出了一种自适应群体结构的粒子群优化算法.算法初期粒子空间分布分散,搜索过程中不断产生新的连接,群体的搜索模式由Lbest  模型逐渐进化为Gbest  模型,群体结构的这种进化方式有利于算法早期的“勘探”和后期的“开采”.实验结果表明,新算法在收敛性能上获得了较大提高.
Abstract:
In particle swarm optimization, the organization mode of population structure directly determines the information sharing and exchange between the particles which produce great influence. Based on the principle of dynamics in the formation process of the complex network, a new particle swarm optimization algorithm was proposed, which is a self-adaptive population structure. At the initial stages of the algorithm, particles are more dispersed with small amount of random connections and search mode is single. With the generation of new connections, the search mode of particles was gradually evolved from Lbest model to Gbest model, which is beneficial to better optimization performance. The evolution of population structure is conducive to early “exploration” and late “exploitation” of the algorithm. The experimental results show that the convergence performance of the new algorithm has been greatly improved.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2012-11-26.     网络出版日期:2013-06-03. 
基金项目:河南省高等教育省级重点研究基金资助项目(2009SJGJX360);河南省教育厅自然科学研究计划资助项目(2011C520018).
通信作者:孙文新.E-mail:sunwxin126@126.com.
作者简介:
孙文新,女,1966年生,副教授,主要研究方向为智能计算和计算机应用.获“市级优秀教师”称号.参编高等教育十二五规划计算机专业教材3部,获得国家发明2项,发表学术论文16篇. 
穆华平,女,1979年生,讲师,主要研究方向为智能计算,发表学术论文5篇.
更新日期/Last Update: 2013-09-27