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Fuzzy Adaptive NSGA-III for Large-Scale Optimization Problems

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Abstract

More and more multi-objective evolutionary algorithms are proposed and used to solve many-objective optimization problems and large-scale optimization problems. However, most of the existing algorithms use fixed crossover probability (pc) and mutation probability (pm) in the generation process of offspring, which makes the performance of the algorithm poor when dealing with complex problems. In this paper, by analyzing the complex non-linear relationship between performance metrics and pc and pm in the search process of many-objective evolutionary algorithms. A fuzzy inference system is constructed to dynamically update the pc and pm in the iterative process. Therefore, a fuzzy adaptive NSGA-III algorithm is proposed and used to solve large-scale optimization problems. For the construction of fuzzy systems, this paper takes the number of iterations, convergence metric, and diversity metric as inputs, and pc and pm as outputs. Four different fuzzy systems are obtained, and the best fuzzy system is selected through experiments. In order to further verify the effectiveness of the algorithm, the proposed algorithm and the existing literature are tested on LSMOP problems. The results show that the fuzzy system can well describe the complex non-linear relationship between the performance metrics and the pc and the pm in the search process of the many-objective evolutionary algorithm. It also effectively improves the performance of the algorithm when solving large-scale optimization problems, resulting in maintenance of the convergence and diversity of the population.

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Funding

This work was supported in part by the National Natural Science Foundation of China (Nos. 11371130, 12071179), 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 (3-502Z20206020), The Open Fund of Key Laboratory of Applied Mathematics of Fujian Province University (Putian University) (No. SX201906) and Digital Fujian Big Data Modeling and Intelligent Computing Institute, Pre-Research Fund of Jimei University.

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Authors and Affiliations

  1. College of Science, Jimei University, Xiamen, 361021, Fujian, China

    Shanli Zhang, Jialiang Xie & Honghui Wang

Authors
  1. Shanli Zhang
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  2. Jialiang Xie
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  3. Honghui Wang
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Contributions

SZ completed the experiment and the first draft and JX gave the overall framework of the paper and introduction. HW modified the grammar of the paper.

Corresponding author

Correspondence to Jialiang Xie.

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All authors declare that they have no conflict of interest.

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Zhang, S., Xie, J. & Wang, H. Fuzzy Adaptive NSGA-III for Large-Scale Optimization Problems. Int. J. Fuzzy Syst. 24, 1619–1633 (2022). https://doi.org/10.1007/s40815-021-01220-9

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  • DOI: https://doi.org/10.1007/s40815-021-01220-9

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