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Accelerating differential evolution based on a subset-to-subset survivor selection operator

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Abstract

Differential evolution (DE) is one of the most powerful and effective evolutionary algorithms for solving global optimization problems. However, just like all other metaheuristics, DE also has some drawbacks, such as slow and/or premature convergence. This paper proposes a new subset-to-subset selection operator to improve the convergence performance of DE by randomly dividing target and trial populations into several subsets and employing the ranking-based selection operator among corresponding subsets. The proposed framework gives more survival opportunities to trial vectors with better objective function values. Experimental results show that the proposed method significantly improves the performance of the original DE algorithm and several state-of-the-art DE variants on a series of benchmark functions.

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Acknowledgements

This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/K001310/1 and the self-determined research funds of CCNU from the colleges’ basic research and operation of MOE (No. CCNU15A05063).

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Authors and Affiliations

  1. School of Computer Science, Central China Normal University, Wuhan, 430079, China

    Jinglei Guo & Zhijian Li

  2. Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, Leicester, LE1 9BH, UK

    Shengxiang Yang

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  1. Jinglei Guo
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  2. Zhijian Li
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Correspondence to Jinglei Guo.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by V. Loia.

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Guo, J., Li, Z. & Yang, S. Accelerating differential evolution based on a subset-to-subset survivor selection operator. Soft Comput 23, 4113–4130 (2019). https://doi.org/10.1007/s00500-018-3060-x

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  • DOI: https://doi.org/10.1007/s00500-018-3060-x

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