A novel multi-objective particle swarm optimization with multiple search strategies

被引:380
作者
Lin, Qiuzhen [1 ]
Li, Jianqiang [1 ]
Du, Zhihua [1 ]
Chen, Jianyong [1 ]
Ming, Zhong [1 ]
机构
[1] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China
关键词
Multiple objective programming; Particle swarm optimization; Evolutionary computation; Multiple search strategies; EVOLUTIONARY ALGORITHMS; OBJECTIVES;
D O I
10.1016/j.ejor.2015.06.071
中图分类号
C93 [管理学];
学科分类号
120117 [社会管理工程];
摘要
Recently, multi-objective particle swarm optimization (MOPSO) has shown the effectiveness in solving multi-objective optimization problems (MOPs). However, most MOPSO algorithms only adopt a single search strategy to update the velocity of each particle, which may cause some difficulties when tackling complex MOPs. This paper proposes a novel MOPSO algorithm using multiple search strategies (MMOPSO), where decomposition approach is exploited for transforming MOPs into a set of aggregation problems and then each particle is assigned accordingly to optimize each aggregation problem. Two search strategies are designed to update the velocity of each particle, which is respectively beneficial for the acceleration of convergence speed and the keeping of population diversity. After that, all the non-dominated solutions visited by the particles are preserved in an external archive, where evolutionary search strategy is further performed to exchange useful information among them. These multiple search strategies enable MMOPSO to handle various kinds of MOPs very well. When compared with some MOPSO algorithms and two state-of-the-art evolutionary algorithms, simulation results show that MMOPSO performs better on most of test problems. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.
引用
收藏
页码:732 / 744
页数:13
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