[1]HU Jie,FAN Qinqin,WANG Zhihuan.Multimodal multi-objective optimization combining zoning and local search[J].CAAI Transactions on Intelligent Systems,2021,16(4):774-784.[doi:10.11992/tis.202010026]
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Multimodal multi-objective optimization combining zoning and local search

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 4 Page number: 774-784 Column: 学术论文—人工智能基础 Public date: 2021-07-05
Title:
Multimodal multi-objective optimization combining zoning and local search
Author(s):
HU Jie1;  FAN Qinqin1; 2;  WANG Zhihuan1
1. Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China;
2. Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai JiaoTong University, Shanghai 200240, China
Keywords:
multimodal multi-objective optimization; zoning search; local search; covariance matrix adaptation evolutionary strategy; population diversity; equivalent solutions; multimodal multi-objective particle swarm optimization
CLC:
TP301.6
DOI:
10.11992/tis.202010026
Abstract:
To maintain population diversity and find a sufficient number of equivalent solutions in multimodal multi-objective optimization, a multimodal multi-objective particle swarm optimization algorithm with zoning and local searches (ZLS-SMPSO-MM) is proposed in this study. In the proposed algorithm, which is based on zoning search and local search, the entire search space is divided into several subspaces to maintain population diversity and reduce search difficulty. Subsequently, an existing self-organizing multimodal multi-objective particle swarm algorithm is used to search equivalent solutions and mine neighborhood information in each subspace, and the covariance matrix adaptation algorithm, which has a better local search ability, is utilized for a refined search in promising regions. Lastly, the performance of ZLS-SMPSO-MM is tested on 14 multimodal multi-objective optimization problems and compared with that of other five state-of-the-art algorithms. Experimental results show that the proposed algorithm can find more equivalent solutions in the decision space and its overall performance is better than that of the compared algorithms.
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