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基于群落动态分配的粒子群优化算法
引用本文:周鹏,李志良,朱磊.基于群落动态分配的粒子群优化算法[J].计算机工程与应用,2007,43(8):71-73,84.
作者姓名:周鹏  李志良  朱磊
作者单位:1. 重庆大学,化学化工学院,重庆,400044
2. 重庆大学,软件工程学院,重庆,400044
基金项目:重庆大学校科研和教改项目
摘    要:通过定义三类群落规划算子:合并算子、融合算子和裂分算子,实现了粒子群优化算法进程中的群落动态分配思想,从而构造了一种新的随机优化技术:基于群落动态分配的粒子群优化算法(Community Dynamic Assignation-based Particle Swarm Optimization,CDAPSO)。新算法通过动态改变粒子群体的组织结构和分配特征来维持寻优过程中启发信息的多样性,从而使其全局收搜索能力得到了显著提高,并且能够有效避免早熟收敛问题。

关 键 词:群体智能算法  粒子群优化算法  群落动态分配  群落规划算子
文章编号:1002-8331(2007)08-0071-03
修稿时间:2006-08

Community dynamic assignation-based particle swarm optimization
ZHOU Peng,LI Zhi-liang,ZHU Lei.Community dynamic assignation-based particle swarm optimization[J].Computer Engineering and Applications,2007,43(8):71-73,84.
Authors:ZHOU Peng  LI Zhi-liang  ZHU Lei
Affiliation:1.College of Chemistry and Chemical Engineering,Chongqing University,Chongqing 400044,China ;2.College of Software Engineering,Chongqing University,Chongqing 400044,China
Abstract:Three kinds of programming operators involving combination operator,harmony operator and abruption operator have been defined to perform community dynamic distribution in Particle Swarm Optimization.Thus a new random optimization tool has been developed,i.e.Community Dynamic Assignation-based Particle Swarm Optimization(CDAPSO).Via changing organization structure and distribution character of particle swarm to increase varieties of eliciting information during optimization,this algorithm notably improves global searching abilities and effectively avoids problems of premature and convergence.
Keywords:swarm intelligence algorithm  Particle Swarm Optimization(PSO)  Community Dynamic Assignation(CDA)  community programming operator
本文献已被 CNKI 维普 万方数据 等数据库收录!
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