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A multiobjective decomposition evolutionary algorithm with optimal history-based neighborhood adaptation and a dual-indicator selection strategy

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Abstract

Neighborhood selection is an important part of a multiobjective evolutionary algorithm based on decomposition (MOEA/D) because the impetus for population evolution mainly comes from its neighborhood. However, the fixed neighborhood size used in MOEA/D may deteriorate the performance of the algorithm due to an unreasonable allocation of computational resources. To further improve the performance of MOEA/D, this paper proposes a multiobjective decomposition evolutionary algorithm with optimal history-based neighborhood adaptation and a dual-indicator selection strategy. The optimal history-based neighborhood adaptation strategy is applied to alleviate the imbalance between exploration and exploitation in the search process, while the dual-indicator selection strategy is developed to enhance the population diversity. The performance of the proposed algorithm is evaluated on the DTLZ and WFG series test problems. Experimental results show that the proposed algorithm performs competitively in comparison with several MOEA/D variants.

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Acknowledgements

This research is partly supported by the National Natural Science Foundation of China under Project Code (62176146, 62272384, 61773314), and the Natural Science Basic Research Program of Shaanxi (Program No. 2020JM-709).

Funding

This research is partly supported by the National Natural Science Foundation of China under Project Code (62176146, 62272384, 61773314), and the Natural Science Basic Research Program of Shaanxi (Program No. 2020JM-709).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Wei Li, Junqing Yuan, Qiaoyong Jiang & Lei Wang

  2. Shaanxi Key Laboratory for Network Computing and Security Technology, Xi’an, 710048, China

    Wei Li & Lei Wang

  3. College of Information and Communication, National University of Defense Technology, Wuhan, 430035, China

    Qingzheng Xu

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  1. Wei Li
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Contributions

All authors contributed to the study conception and design. The algorithm design and implementation were performed by WL and JY. Experiment and analysis were performed by QJ and QX. LW revised the manuscript. All authors read and approved the final manuscript.

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Correspondence to Junqing Yuan.

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Li, W., Yuan, J., Jiang, Q. et al. A multiobjective decomposition evolutionary algorithm with optimal history-based neighborhood adaptation and a dual-indicator selection strategy. Cluster Comput 26, 3319–3339 (2023). https://doi.org/10.1007/s10586-022-03736-7

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  • DOI: https://doi.org/10.1007/s10586-022-03736-7

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