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Weight grouping operators selection strategy for a multiobjective evolutionary algorithm based on decomposition

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Abstract

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) works by using a set of uniformly distributed weight vectors to decompose a multiobjective optimization problem (MOP) into multiple single objective optimization subproblems for simultaneous optimization. Selecting parents from the neighborhood when performing reproduction is highly probable, thus different operators and neighborhoods may produce different offspring. However, one omnipotent operator usually cannot handle all different complex MOPs very well, for this reason, we propose a weight grouping operators selection (WGOS) strategy. Firstly, we divide the weight vectors into several groups and assign different reproduction operators to each group. The subproblems in each group are optimized by using the reproduction operators assigned. Then, the size of each group is dynamically adjusted according to the quality of the generated offspring, and the group of the reproduction operator that generates the better offspring expands, and the group of the reproduction operator that generates the worse offspring shrinks correspondingly until all the groups are merged into one and all adopt the same reproduction operator. This process will eventually pick out one operator for subsequent evolution. Finally, we will verify the performance of the selected operator to decide whether to continue using it or switch to another operator. A large number of comparative experiments have proved this strategy has better performance.

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Data Availability Statements

Data supporting the results of this study are available from the corresponding author upon reasonable request, including source code and experimental results.

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Acknowledgements

The work is partially supported by the National Natural Science Foundation of China (Nos. U1836216, 61772322), the major fundamental research project of Shandong, China(Nos. ZR2020MF042, ZR2019ZD03).

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China

    Lin Shi, Yanyan Tan, Zeyuan Yan, Lili Meng & Li Liu

  2. Institute of Data Science and Technology, Shandong Normal University, Jinan, 250358, China

    Yanyan Tan, Lili Meng & Li Liu

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  1. Lin Shi
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  2. Yanyan Tan
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  3. Zeyuan Yan
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  4. Lili Meng
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  5. Li Liu
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Correspondence to Yanyan Tan.

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Shi, L., Tan, Y., Yan, Z. et al. Weight grouping operators selection strategy for a multiobjective evolutionary algorithm based on decomposition. Appl Intell 53, 10585–10601 (2023). https://doi.org/10.1007/s10489-022-03900-x

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