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An enhanced decomposition-based multiobjective evolutionary algorithm with adaptive neighborhood operator and extended distance-based environmental selection

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Abstract

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) proposed in 2007 has shown to be effective in solving multiobjective optimization problems. However, the fixed neighborhood size prevents MOEA/D series algorithm from effective exploring and exploiting. Meanwhile, since the newly generated offspring can replace the parent solutions in the neighborhood without any constraints, the current solutions of multiple subproblems may converge to a certain region or even a certain point. Obviously, the population diversity is significantly deteriorate. To alleviate these problems, this paper proposes an enhanced MOEA/D with adaptive neighborhood operator and extended distance-based environmental selection (MOEA/D-ANED) to solve many-objective optimization problems (MaOPs). The basic idea is that the adaptive neighborhood operator based on utility assignment is helpful for the algorithm to select a more promising solution, thereby enhancing the search performance in many-objective optimization. In addition, the extended distance-based environmental selection strategy is developed to considerably improve the accuracy and effectiveness of updating the parent solutions of different subproblems. Experimental results on two widely used test suits demonstrate the competitiveness of the proposed algorithm in terms of solution accuracy compared with eight state-of-the-art multiobjective optimization algorithms.

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Acknowledgements

This research is partly supported by the National Natural Science Foundation of China under Project Code (62176146, 61773314), and the Special project of Education Department of Shaanxi Provincial Government for Local Services (Program No. 21JC026).

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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 & Lei Wang

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

    Wei Li & Lei Wang

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  1. Wei Li
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  2. Junqing Yuan
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  3. Lei Wang
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All authors contributed to the study conception and design. The algorithm design and implementation were performed by WL. Experiment and analysis were performed by JY. 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. & Wang, L. An enhanced decomposition-based multiobjective evolutionary algorithm with adaptive neighborhood operator and extended distance-based environmental selection. J Supercomput 79, 12341–12393 (2023). https://doi.org/10.1007/s11227-023-05118-1

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