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A decomposition based multiobjective evolutionary algorithm with self-adaptive mating restriction strategy

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Abstract

MOEA/D decomposes the multiobjective optimization problem into a number of subproblems. However, one subproblem’s requirement for exploitation and exploration varies with the evolutionary process. Furthermore, different subproblems’ requirements for exploitation and exploration are also different as the subproblems have been solved in distinct degree. This paper proposes a decomposition based multiobjective evolutionary algorithm with self-adaptive mating restriction strategy (MOEA/D-MRS). Considering the distinct solved degree of the subproblems, each subproblem has a separate mating restriction probability to control the contributions of exploitation and exploration. Besides, the mating restriction probability is updated by the survival length at each generation to adapt to the changing requirements. The experimental results validate that MOEA/D-MRS performs well on two test suites.

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Funding

This study was funded by China Aerospace Science and Technology Innovation Foundation (Grant number: CAST.No.JZ20160008), National Natural Science Foundation of China (Grant number: 61333003) and National Natural Science Foundation of China (Grant number: 61703382).

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Authors and Affiliations

  1. Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin, 150001, China

    Xin Li & Shenmin Song

  2. Beijing Electro-Mechanical Engineering Institute, Beijing, 100074, China

    Hu Zhang

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  1. Xin Li
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  3. Shenmin Song
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Correspondence to Shenmin Song.

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Li, X., Zhang, H. & Song, S. A decomposition based multiobjective evolutionary algorithm with self-adaptive mating restriction strategy. Int. J. Mach. Learn. & Cyber. 10, 3017–3030 (2019). https://doi.org/10.1007/s13042-018-00919-w

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