[1]王? 艳,曾建潮.多目标微粒群优化算法综述[J].智能系统学报,2010,5(5):377-384.[doi:10.3969/j.issn.1673-4785.2010.05.001]
WANG Yan,ZENG Jian-chao.A survey of a multiobjective particle swarm optimization algorithm[J].CAAI Transactions on Intelligent Systems,2010,5(5):377-384.[doi:10.3969/j.issn.1673-4785.2010.05.001]
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WANG Yan,ZENG Jian-chao.A survey of a multiobjective particle swarm optimization algorithm[J].CAAI Transactions on Intelligent Systems,2010,5(5):377-384.[doi:10.3969/j.issn.1673-4785.2010.05.001]
多目标微粒群优化算法综述
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
5
期数:
2010年第5期
页码:
377-384
栏目:
综述
出版日期:
2010-10-25
- Title:
- A survey of a multiobjective particle swarm optimization algorithm
- 文章编号:
- 1673-4785(2010)05-00377-08
- Keywords:
- multi-objective optimization; particle swarm optimization; non-dominated solutions; archive; diversity
- 分类号:
- TP18
- 文献标志码:
- A
- 摘要:
- 作为一种有效的多目标优化工具，微粒群优化(PSO)算法已经得到广泛研究与认可.首先对多目标优化问题进行了形式化描述，介绍了微粒群优化算法与遗传算法的区别，并将多目标微粒群优化算法(MOPSO)分为以下几类：聚集函数法、基于目标函数排序法、子群法、基于Pareto支配算法和其他方法，分析了各类算法的主要思想、特点及其代表性算法.其次，针对非支配解的选择、外部档案集的修剪、解集多样性的保持以及微粒个体历史最优解和群体最优解的选取等热点问题进行了论述，并在此基础上对各类典型算法进行了比较.最后，根据当前MOPSO算法的研究状况，提出了该领域的发展方向.
- Abstract:
- Particle swarm optimization (PSO) algorithms have been widely studied and approved as effective multi-objective paper optimizers. In this paper， first of all multi-objective problems were formally described, and the difference between a PSO and genetic algorithm (GA) was introduced. Then the taxonomy of current multi-objective PSO (MOPSO) algorithms, which include aggregate functions, sorting based on objective functions, sub-population methods, Pareto dominated based algorithms, and other algorithms, was presented. Additionally, the main ideas, features, and representative algorithms of each approach were analyzed. Secondly, hot topics in MOPSO algorithms such as selecting non-dominated solutions, pruning archive sets, maintaining the diversity of the solutions set, and selecting both the best personal and global solutions were discussed on the basis of which all typical algorithms were compared. Finally, several viewpoints for the future research of MOPSO were proposed according to the present studies.
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备注/Memo
收稿日期：2009-09-22.
基金项目：国家自然科学基金资助项目（60674104）.
通信作者：曾建潮.E-mail:zengjianchao@263.net.
作者简介：
王?? 艳，女，1975年生，博士研究生，讲师.主要研究方向为智能计算、多目标优化等，发表学术论文近10篇.
曾建潮，男，1963年生，教授、博士生导师、博士，中国自动化学会系统仿真专业委员会副主任委员，中国计算机学会Petri网专业委员会委员，山西省系统工程学会、山西省自动化学会和计算机学会副理事长，山西省自动化学会学术委员会主任.主要研究方向为智能计算、复杂系统建模与仿真等.承担或完成包括国家自然科学基金、国家科技攻关项目等30余项，获山西省科技进步奖、自然科学奖5项.发表学术论文300余篇，被SCI、EI检索100余篇，出版专著3部.
更新日期/Last Update:
2010-11-24