[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(05):377-384.[doi:10.3969/j.issn.1673-4785.2010.05.001]
 WANG Yan,ZENG Jian-chao.A survey of a multiobjective particle swarm optimization algorithm[J].CAAI Transactions on Intelligent Systems,2010,5(05):377-384.[doi:10.3969/j.issn.1673-4785.2010.05.001]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第5卷
期数:
2010年05期
页码:
377-384
栏目:
出版日期:
2010-10-25

文章信息/Info

Title:
A survey of a multiobjective particle swarm optimization algorithm
文章编号:
1673-4785(2010)05-00377-08
作者:
王  艳12曾建潮2
(1.兰州理工大学 电信工程学院,甘肃 兰州 730050; 2.太原科技大学 复杂系统和智能计算实验室,山西 太原 030024)
Author(s):
WANG Yan12 ZENG Jian-chao2
(1.College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China; 2.Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan 030024, China)
关键词:
多目标优化 微粒群优化算法非支配解外部档案多样性
Keywords:
multi-objective optimization particle swarm optimization non-dominated solutions archive diversity
分类号:
TP18
DOI:
10.3969/j.issn.1673-4785.2010.05.001
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.

参考文献/References:

[1]MOORE J, CHAPMAN R. Application of particle swarm to multiobjective optimization[R]. Auburn, USA: Department of Computer Science and Software Engineering, Auburn University, 1999.
[2]COELLO COELLO C A, LECHUGA M S. MOPSO: a proposal for multiple objective particle swarm optimization[C]//Congress on Evolutionary Computation(CEC’2002). Honolulu, USA, 2002, 2: 1051-1056.
[3]RAY T, KANG T, CHYE S K. An evolutionary algorithm for constrained optimization[C]//Proceedings of the Genetic and Evolutionary Computation Conference. Las Vegas, USA, 2000: 771-777.
[4]REYES-SIERRA M, COELLO COEELO C A. Multi-objective particle swarm optimizers: a survey of the state-op-the-art [J]. International Journal of Computational Intelligence Research,2006, 2(3): 287-308.
[5]COELLO COELLO C A, PULIDO G T, LECHUGA M S. Handling multiple objectives with particle swarm optimization[J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 256-279.
[6]COELLO COELLO C A, PULIDO G T. Using clustering techniques to improve the performance of a multi-objective particle swarm optimizer[J]. Lecture Notes in Computer Science, 2004, 3102: 225-237.
[7]CAGNINA L, ESQUIVEL S, COELLO COELLO C A. A particle swarm optimizer for multi-objective optimization [J]. Journal of Computer Science & Technology, 2005, 5(4): 204-210.
[8]PARSOPOULOS K E, VRAHATIS M N. Particle swarm optimization method in multiobjective problems[C]//Proceedings of ACM Symposium on Applied Computing(SAC 2002). Madrid, Spain, 2002: 603-607.
[9]HU Xiaohui, EBERHART R C, SHI Yuhui. Particle swarm with extended memory for multiobjective optimization[C]//IEEE Swarm Intelligence Symposium. Indianapolis, USA, 2003: 193-197.
[10]郑友莲,樊俊青.多目标粒子群优化算法研究[J].湖北大学学报:自然科学版, 2008, 30(4): 351-355. 
 ZHENG Youlian, FAN Junqing. Study on multiobjective particle swarm optimization algorithm[J]. Journal of Hubei University: Natural Science, 2008, 30(4): 351-355.
[11]曾建潮,介婧,崔志华.微粒群算法[M]. 北京:科学出版社, 2004: 9-13.
[12]郑金华.多目标进化算法及其应用[M]. 北京:科学出版社, 2007: 9-10.
[13]BAUMGARTNER U, MAGELE Ch, RENHART W. Pareto optimality and particle swarm optimization[J]. IEEE Transactions on Magnetics, 2004, 40(2): 1172-1175.
[14]张利彪,周春光,马铭,等.基于粒子群算法求解多目标优化问题[J].计算机研究与发展, 2004, 41(7): 1286-1291.
 ZHANG Libiao, ZHOU Chunguang, MA Ming, et al. Solutions of multiobjective optimization problems based on particle swarm optimization[J]. Journal of Computer Research and Development, 2004, 41(7): 1286-1291.
[15]HU Xiaohui, EBERHART R C. Multiobjective optimization using dynamic neighborhood particle swarm optimization[C]//Proceedings of the 2002 Congress on Evolutionary Computation. Honolulu, USA, 2002: 1677-1681.
[16]PARSOPOULOS K E, TASOULIS D K, VRAHATIS M N. Multiobjective optimization using parallel vector evaluated particle swarm optimization[C]//Proceedings of the IASTED International Conference on Artificial Intelligence and Applications(AIA2004). Innsbruck, Austria, 2004: 823-828.
[17]张利彪,周春光,刘小华,等.求解多目标优化问题的一种多子群体进化算法[J].控制与决策, 2007, 22(11): 1313-1320.
ZHANG Libiao, ZHOU Chunguang, LIU Xiaohua, et al. A multiple subswarms evolutionary algorithm for multiobjective optimization problems[J]. Control and Decision, 2007, 22(11): 1313-1320.
[18]PULIDO G T, COELLO COELLO C A. Using clustering techniques to improve the performance of a particle swarm optimizer[C]//Proceedings of the Genetic and Evolutionary Computation Conference. Seattle, USA, 2004: 225-237.
[19]熊盛武,刘麟,王琼,等.改进的多目标粒子群算法[J].武汉大学学报:理学版, 2005, 51(3): 308-312.
XIONG Shengwu, LIU Lin, WANG Qiong, et al. Improved multiobjective particle swarm algorithm[J]. Wuhan University Journal: Natural Science Edition, 2005, 51(3): 308-312.
[20]CHAMAANI S, MIRTAHERI S A, TESHNEHLAB M, et al. Modified multi-objective particle swarm optimization for electromagnetic absorber design[J]. Progress in Electromagnetics Research, 2008, 79: 353-366.
[21]杨俊杰,周建中,方仍存,等. 基于自适应网格的多目标粒子群优化算法[J].系统仿真学报, 2008, 20(21): 5843-5847.
YANG Junjie, ZHOU Jianzhong, FANG Rengcun, et al. Multiobjective particle swarm optimization based on adaptive grid algorithms[J]. Journal of System Simulation, 2008, 20(21): 5843-5847.
[22]ZITZLER E, LAUMANNS M, THIELE L. SPEA2: improving the strength Pareto evolutionary algorithm[R]. Zurich, Swiss: ETH Zurich, 2001.
[23]PULIDO G T, COELLO COELLO C A, SANTANA-QUINTERO L V. EMOPSO: a multi-objective particle swarm optimizer with emphasis on efficiency[C]//Proceedings of 4th International Evolutionary Multi-criterion Optimization. Matsushima, Japan, 2007: 272-285.
[24]LAUMANNS M, THIELE L, DEB K, et al. Combining convergence and diversity in evolutionary multi-objective optimization[J]. Evolutionary Computation, 2002, 10(3): 263-282.
[25]MOSTAGHIM S, TEICH J. The role of dominance in multi-objective particle swarm optimization methods[C]//Proceedings of the 2003 IEEE Swarm Intelligence Symposium. Indianapolis, USA, 2003: 26-33.
[26]蒋浩,郑金华,陈良军.一种求解多目标优化问题的粒子群算法[J].模式识别与人工智能, 2007, 20(5): 606-610.
JIANG Hao, ZHENG Jinhua, CHEN Liangjun. A particle swarm algorithm for multi-objective problem[J]. Pattern Recognition and Artificial Intelligence, 2007, 20(5): 606-610.
[27]金欣磊,马龙华,刘波,等.基于动态交换策略的快速多目标粒子群优化算法研究[J].电路与系统学报, 2007, 12(2): 78-82.
JIN Xinlei, MA Longhua, LIU Bo, et al. A fast multi-objective particle swarm optimization based on dynamic exchange strategy[J]. Journal of Circuits and Systems, 2007, 12(2): 78-82.
[28]李宁,邹彤,孙德宝,等. 基于粒子群的多目标优化算法[J]. 计算机工程与应用, 2005, 23: 43-46.
LI Ning, ZOU Tong, SUN Debao, et al. Multiobjective optimization utilizing particle swarm[J]. Computer Engineering and Applications, 2005, 23: 43-46.
[29]SALAZARLECHUGA M, ROWE J E. Particle swarm optimization and fitness sharing to solve multi-objective optimization problems[C]//Proceedings of Congress on Evolutionary Computation. Edinburgh, UK, 2005: 1204-1211.
[30]王小刚,李明杰,王福利,等,一种新的多目标粒子群算法的研究与应用[J].东北大学学报:自然科学版, 2008, 29(10): 1377-1380.
WANG Xiaogang, LI Mingjie, WANG Fuli, et al. Study on a new MOPSO and its applications[J]. Journal of Northeastern University: Natural Science, 2008, 29(10): 1377-1380.
[31]李中凯,谭建荣,冯毅雄,等.基于拥挤距离排序的多目标粒子群优化算法及其应用[J].计算机基础制造系统, 2008, 14(7): 1329-1336.
LI Zhongkai, TAN Jianrong, FENG Yixiong, et al. Multiobjective particle swarm optimization algorithm based on crowding distance sorting and its application[J]. Computer Integrated Manufacturing Systems, 2008, 14(7): 1329-1336.
[32]王辉,钱锋.基于拥挤度与变异的动态微粒群多目标优化算法[J].控制与决策, 2008, 23(11): 1238-1242.
 WANG Hui, QIAN Feng. Improved PSO-based multi-objective- optimization by crowding with mutation and particle swarm optimization dynamic changing[J]. Control and Decision, 2008, 23(11): 1238-1242.
[33]王洪刚,马良,李高雅. 多目标微粒群优化算法[J].计算机工程与应用, 2008, 44(34): 64-66.
WANG Honggang, MA Liang, LI Gaoya. Multiobjective particle swarm optimization[J]. Computer Engineering and Applications, 2008, 44(34): 64-66.
[34]宋武,郑金华.基于密度熵的多目标粒子群算法[J].计算机工程与应用, 2007, 43(26): 41-44.
SONG Wu, ZHENG Jinhua. MOPSO algorithm based on density entropy[J]. Computer Engineering and Applications, 2007, 43(26): 41-44.
[35]孙小强,张求明.一种基于粒子群优化的多目标优化算法[J].计算机工程与应用, 2006, 18: 40-42.
SUN Xiaoqiang, ZHANG Qiuming. A particle swarm optimization method for multi-objective optimization[J]. Computer Engineering and Applications, 2006, 18: 40-42.
[36]JANSON S, MERKLE D. A new multiobjective particle swarm optimization using clustering applied to automated docking[C]//Proceedings of Hybrid Metaheuristics. Barcelona, Spain, 2005: 128-1.
[37]王俊年,刘建勋,陈湘州.一种多目标微粒群算法及其收敛性分析[J].计算机工程与应用, 2007, 43(22): 53-.
WANG Junnian, LIU Jianxun, CHEN Xiangzhou. Multiobjective particle swarm optimization and its convergence analysis[J]. Computer Engineering and Applications, 2007, 43(22): 53-55.
[38]胡德峰,张步涵,姚建光.基于改进粒子群算法的多目标最优潮流计算[J].电力系统及其自动化学报, 2007, 19(3): 51-57.
HU Defeng, ZHANG Buhan, YAO Jianguang. Improved particle swarm optimization algorithm for multiobjective optimal power flow[J]. Proceedings of CSU-EPSA, 2007, 19(3): 51-57.

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备注/Memo

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