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A modified multifactorial differential evolution algorithm with optima-based transformation

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Abstract

Multifactorial evolution algorithm (MFEA) is a powerful search paradigm with the purpose of addressing multiple optimization tasks simultaneously in the field of evolutionary computation. The assortative mating of MFEA is a key component to make it outperform traditional single-task optimization algorithms. However, the optimal solution of each generation has still not been well utilized to accelerate convergence in the process of assortative mating operation for existing MFEAs. This paper proposes a multifactorial differential evolution algorithm with optima-based transformation (MFDE-OBT), which employs the optimal solution of each generation to design an improved assortative mating operation based on the DE/rand/2 mutation. The improved operation generates an offspring for each individual by running a perturbation around a current optimal individual. The object of the assortative mating is a vector added for the perturbation, which is the sum of two difference vectors generated by a random sample with a certain probability from an individual set that can be same as or different from the one of the task involving the optimal individual. In addition, MFDE-OBT integrates an opposition-based search strategy behind the assortative mating operation to balance exploitation and exploration of each search space. Experimental results on benchmark problems constituted by tasks with different degree of similarity and intersection demonstrate the advantage of the proposed MFDE-OBT algorithm over some state-of-the-art algorithms, in terms of solution precision and convergence performance.

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Acknowledgements

This work was supported in part by the State Key Laboratory of Biogeology and Environmental Geology (China University of Geosciences, No. GBL21801), the National Nature Science Foundation of China (No. 61972136), Hubei Key Laboratory of Transportation Internet of Things, China (No. WHUTIOT-2019001).

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Authors and Affiliations

  1. School of Information and Mathematics, Yangtze University, Jingzhou, 434000, Hubei, China

    Lingyi Shi, Zhongbo Hu & Qinghua Su

  2. Department of Groundtoair Navigation, Air Force Communication NCO Academy, Dalian, 116000, Liaoning, China

    Yongfei Miao

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  1. Lingyi Shi
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  2. Zhongbo Hu
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  3. Qinghua Su
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  4. Yongfei Miao
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Correspondence to Zhongbo Hu.

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Shi, L., Hu, Z., Su, Q. et al. A modified multifactorial differential evolution algorithm with optima-based transformation. Appl Intell 53, 2989–3001 (2023). https://doi.org/10.1007/s10489-022-03537-w

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