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A parametric segmented multifactorial evolutionary algorithm based on a three-phase analysis

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Abstract

Evolutionary multitasking optimization, which concentrates on solving multiple tasks simultaneously, has been a core area of interest for researchers in recent years. Existing Multifactorial Evolutionary Algorithms (MFEA) are quite dependent on the synergy among the tasks. Consequently, solving multiple tasks is prone to fall into local traps when the optimization enters a certain stage. The objective of this paper is to investigate these problems in more detail and provide corresponding solutions. Specifically, we propose a three-stage analysis method that divides the multitasking optimization problem into three stages and explain the MFEA features according to the individual distribution in each stage, based on which we further develop a Parametric Segmented Multifactorial Evolutionary Algorithm (PS-MFEA) and apply a precise search strategy in the algorithm. Additionally, we propose both a reinitialization mechanism and a backtracking mechanism to avoid local optima. We conduct a comprehensive experiment on two test sets of benchmark problems with different similarity levels and the discrete combinatorial optimization tasks of the traveling salesman problem. The results demonstrate that using PS-MFEA can obtain better performances in both test sets and solve practical problems.

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Acknowledgements

Thanks to Eneko Osaba for providing the dMFEA-II source code. Thanks to A. Gupta and others for providing the MFEA open-source code. This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61836005 and 62176225.

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Authors and Affiliations

  1. Department of Automation, Xiamen University, Xiamen, 361005, China

    Peihua Chai, Langcai Cao & Ridong Xu

  2. Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-making, Xiamen, 361005, China

    Peihua Chai & Langcai Cao

  3. Department of Computer and Information Sciences, Northumbria University, Newcastle, NE1 8ST, UK

    Yifeng Zeng

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  1. Peihua Chai
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  2. Langcai Cao
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Correspondence to Langcai Cao.

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We have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper - A Parametric Segmented Multifactorial Evolutionary Algorithm Based on Three-Phase Analysis. We have no any financial interests/personal relationships which may be considered as potential competing interests. Peihua Chai, Langcai Cao, Ridong Xu and Yifeng Zeng

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Chai, P., Cao, L., Xu, R. et al. A parametric segmented multifactorial evolutionary algorithm based on a three-phase analysis. Appl Intell 53, 25605–25625 (2023). https://doi.org/10.1007/s10489-023-04917-6

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