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Gray wolf optimization-based self-organizing fuzzy multi-objective evolution algorithm

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Abstract

Two goals of multi-objective evolutionary algorithms are effectively improving their convergence and diversity and making the Pareto set evenly distributed and close to the real Pareto front. At present, the challenges to be solved by the multi-objective evolutionary algorithm are to improve the convergence and diversity of the algorithm, and how to better solve functions with complex PF and/or PS shapes. Therefore, this paper proposes a gray wolf optimization-based self-organizing fuzzy multi-objective evolutionary algorithm. Gray wolf optimization algorithm is used to optimize the initial weights of the self-organizing map network. New neighborhood relationships for individuals are built by self-organizing map, which can maintain the invariance of feature distribution and map the structural information of the current population into Pareto sets. Based on this neighborhood relationship, this paper uses the fuzzy differential evolution operator, which constructs a fuzzy inference system to dynamically adjust the weighting parameter in the differential operator, to generate a new initial solution, and the polynomial mutation operator to refine them. Boundary processing is then conducted. Experiments on 15 problems of GLT1-6 and WFG1-9 and the algorithm proposed in this paper achieve the best on 18 values. And the result shows that the convergence and diversity of the proposed algorithm are better than several state-of-the-art multi-objective evolutionary algorithms.

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Funding

This work was supported in part by the National Natural Science Foundation of China (Nos. 12271211,12071179), the National Natural Science Foundation of Fujian Province (Nos. 2021J01861), Soft Science Research Program of Fujian Province (No. B19085), the Project of Education Department of Fujian Province (No. JT180263), the Youth Innovation Fund of Xiamen City (3502Z20206020), the Open Fund of Digital Fujian Big Data Modeling and Intelligent Computing Institute, Pre-Research Fund of Jimei University.

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Authors and Affiliations

  1. School of Science, Jimei University, Xiamen, 361021, China

    Jialiang Xie, Shanli Zhang & Honghui Wang

  2. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China

    Dongrui Wu

  3. Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, Wuhan, China

    Dongrui Wu

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  1. Jialiang Xie
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  2. Shanli Zhang
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  3. Honghui Wang
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  4. Dongrui Wu
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Contributions

Jialiang Xie gave the overall framework of the paper and introduction, and Shanli Zhang completed the experiment and the first draft. Honghui Wang modified the grammar of the paper, and Dongrui Wu coordinated the paper.

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Correspondence to Jialiang Xie.

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Xie, J., Zhang, S., Wang, H. et al. Gray wolf optimization-based self-organizing fuzzy multi-objective evolution algorithm. Soft Comput 26, 12077–12092 (2022). https://doi.org/10.1007/s00500-022-07492-z

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