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Efficient constrained large-scale multi-objective optimization based on reference vector-guided evolutionary algorithm

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Abstract

The large-scale multi-objective optimization problem exist widely in reality while they have complex constraints. The simultaneous effect of the large-scale decision variables and the complexity of constraints makes the traditional multi-objective evolutionary algorithm face great challenges. For the large-scale of decision variables, some reference vector-guided, competitive group optimization-based and pairwise child generation-based algorithms have improved the search efficiency of constrained LSMOPs. However, these algorithms encounter difficulties in handling large-scale decision variables and complex constraints at the same time. In this paper, a reference vector-guided with dominance co-evolutionary multi-objective algorithm is proposed to solve constrained large-scale multi-objective problems. First, a reference vector is employed to guide several sub-populations with a fixed number of neighborhood solutions. Then, a new environmental selection is constructed using the angle penalty distance with dominance relationship. This new environmental selection strategy greatly enhances selection pressure. At the same time, a co-evolutionary constraint handling technology is applied to efficiently span the infeasible region. The proposed algorithm is evaluated on constrained large-scale multi-objective problems with 100, 500 and 1000 decision variables. In addition, the impact of each component of the proposed algorithm is examined for the overall performance of the algorithm and tested in a practical application in microgrids. The experimental results demonstrate the effectiveness of the algorithm in constrained large-scale multi-objective optimization.

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Acknowledgements

The authors are very grateful to the anonymous reviewers for their valuable comments on improving this article. Additionally, this work is supported by National Natural Science Foundation of China (No. 62262017, 62202129), Hainan Provincial Natural Science Foundation of China (123MS003), Scientific Research Project of Hunan Provincial Department of Education (No. 21C0922), Open Fund Project of Fujian Provincial Key Laboratory of Data Intensive Computing (No. BD202004), and Open Fund Project of Vehicle Measurement, Control and Safety Key Laboratory of Sichuan Province (No. QCCK2021-006).

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Authors and Affiliations

  1. School of Computer Science and Technology, Hainan University, Haikou, 570228, China

    Chaodong Fan, Laurence T. Yang & Leyi Xiao

  2. School of Software and Information Engineering, Hunan Software Vocational and Technical University, Xiangtan, 411100, China

    Chaodong Fan

  3. School of Computer Science, Xiangtan University, Xiangtan, 411105, China

    Jiawei Wang

  4. Fujian Provincial Key Laboratory of Data Intensive Computing, Quanzhou Normal University, Quanzhou, 3620 0 0, China

    Leyi Xiao

  5. Vehicle Measurement, Control and Safety Key Laboratory of Sichuan Province, Xihua University, Chengdu, 610039, China

    Leyi Xiao

  6. College of Foreign Languages, Inter-Disciplinary Research Center of Language Intelligence and Cultural Heritages, Hunan University, Changsha, 410082, China

    Zhaoyang Ai

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  1. Chaodong Fan
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  2. Jiawei Wang
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Correspondence to Leyi Xiao.

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Fan, C., Wang, J., Yang, L.T. et al. Efficient constrained large-scale multi-objective optimization based on reference vector-guided evolutionary algorithm. Appl Intell 53, 21027–21049 (2023). https://doi.org/10.1007/s10489-023-04663-9

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