[1]陈?? 杰,潘? 峰,王光辉.粒子群优化方法在动态优化中的研究现状[J].智能系统学报,2009,4(3):189-198.
　CHEN Jie,PAN Feng,WANG Guang-ui.Review of the PSO research in dynamic environments[J].CAAI Transactions on Intelligent Systems,2009,4(3):189-198.
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粒子群优化方法在动态优化中的研究现状

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 4 期数: 2009年第3期 页码: 189-198 栏目: 综述 出版日期: 2009-06-25
Title:
Review of the PSO research in dynamic environments
文章编号:
1673-4785（2009)03-0189-10
作者:
陈?? 杰1, 潘? 峰1,2， 王光辉1
1.北京理工大学 复杂系统智能控制与决策教育部重点实验室，北京100081；
2.Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology, Indiana UniversityPurdue University Indianapolis, Indianapolis, IN 46202, USA
Author(s):
CHEN Jie1, PAN Feng1,2， WANG Guang-ui1
1.Key Laboratory of Complex System Intelligent Control and Decision of Ministry of Education, Beijing Institute of Technology, Beijing 100081, China;
2.Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology, Indiana UniversityPurdue University Indianapolis, Indianapolis, IN 46202, USA
关键词:
粒子群优化方法; 动态环境优化; 检测策略; 应对策略; 性能评价
Keywords:
particle swarm optimization (PSO);  optimization in dynamic environment;  detection strategy;  response strategy;  performance evaluation
分类号:
TP391.41
文献标志码:
A
摘要:
作为一种基于群智能的并行随机优化方法，粒子群优化算法（PSO）在优化求解问题中体现出了良好的性能.从提出至今引起了广泛的关注，研究成果也不断涌现.从2000年开始，PSO被用于动态优化问题中.这对PSO的研究提出了新的挑战，对于动态问题的优化不再是在解空间中找到一个最优点，而是要尽可能地在解空间中跟踪运动变化的最优点.对目前为止对于PSO在动态环境优化问题的研究内容进行了分析和总结，介绍了针对动态环境优化问题PSO的改进方法、对环境变化的检测和应对策略、优化性能评价的一系列方法以及各种试验及应用案例.
Abstract:
Particle swarm optimization (PSO), a parallel random optimization method based on swarm intelligence, exhibits good performance for optimization problems. Since 2000, PSO has been applied to optimization problems in dynamic environments. The challenge with PSO is that the objective is not only to locate an optimum, but also to track that moving optimum as closely as possible. This paper presented the latest developments of PSO in dynamic environments. Various research approaches were reviewed, including improvements in PSO, dynamic change detection, response strategies, performance evaluation and experiments used in researching dynamic problems.
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备注/Memo

收稿日期：2008-08-22
基金项目： 高等学校优秀青年教师教学科研奖励计划资助项目（20010248).
通信作者：潘 峰. Email: andropan@gmail.com.
作者简介：
陈陈 杰,男，1965年生，教授、博士生导师、博士，中国自动化学会控制理论专业委员会委员.主要研究方向为复杂系统、多目标优化与决策、智能控制、非线性控制和优化方法等.2005年获“全国优秀科技工作者”荣誉称号，2001年获教育部“全国高校青年教师奖”.获部级科技进步奖12项，北京市优秀教学成果二等奖2项.完成并通过鉴定的科研项目20余项.发表学术论文100余篇，出版教材1部，译著1部.
潘 峰,男，讲师，博士.主要研究方向为智能控制、计算智能、人工智能、伺服系统和机器人等.已获部级科技进步奖1项，发表学术论文20余篇.
王光辉，男，1987年生，博士研究生，主要研究方向为智能算法、计算智能、优化设计.

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