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Improving differential evolution by differential vector archive and hybrid repair method for global optimization

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Abstract

Differential evolution (DE) has been extensively studied in the past decade, though researchers may ignore the effect of an archive storing previous search information. Moreover, boundary repair issue is seldom handled in the literature. This paper attempts to improve the performance of DE algorithm from these two aspects. First, a differential vector archive is constructed and adaptively updated during the optimization process of DE. The archive stores a set of differential vectors representing potential good search directions. Second, inspired by recently reported results about repair methods, a hybrid of four commonly used repair methods is proposed. The hybrid method is more applicable to unknown optimization problems than a single repair method. A test suite containing 28 benchmark functions is employed for experimental investigation. Experimental results show that the proposed algorithm usually affects the search to attain better performance in the later evolutionary stage. Our algorithm significantly outperforms a state- of-the-art algorithm. This result verifies the effectiveness of the proposed algorithm.

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Acknowledgments

This research was supported in part by the Tianjin Thousand Youth Talents Plan Project of Tianjin Normal University (ZX110023) and the Applied Basic Research Program of Tianjin (15JCYBJC51500, 15JCYBJC52300).

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Authors and Affiliations

  1. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Xin Zhang & Xiu Zhang

  2. Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, China

    Xin Zhang & Xiu Zhang

Authors
  1. Xin Zhang
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  2. Xiu Zhang
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Correspondence to Xiu Zhang.

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The authors declare that they have no conflict of interest.

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Communicated by V. Loia.

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Zhang, X., Zhang, X. Improving differential evolution by differential vector archive and hybrid repair method for global optimization. Soft Comput 21, 7107–7116 (2017). https://doi.org/10.1007/s00500-016-2253-4

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  • DOI: https://doi.org/10.1007/s00500-016-2253-4

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