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Rethinking the differential evolution algorithm

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Abstract

Selection operation plays a significant role in differential evolution algorithm. A new differential evolution algorithm based on an improved selection process is presented in this work. It was studied that there was neither a practical method to maintain the distribution of population nor a correction to the variables out of bounds in mutation process in a standard differential evolution algorithm. The fast non-dominated sorting approach and the spatial distance algorithm which were applied to the beginning of the selection process, as well as a method to fix the transboundary variables in the mutation process, were adopted to optimize the differential evolution algorithm. The reformative algorithm could obtain a uniformly distributed and effective Pareto-optimal sets when applied to the classical multi-objective test functions; it performed prominently in the experiment of optimizing the quality, the cost and the time in a construction project compared with the previous work.

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Author information

Authors and Affiliations

  1. Faculty of Earth Resources, China University of Geosciences, Wuhan, 430074, China

    Hongwei Liu

  2. School of Computer Science, China University of Geosciences, Wuhan, 430074, China

    Xiang Li & Wenyin Gong

Authors
  1. Hongwei Liu
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  2. Xiang Li
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  3. Wenyin Gong
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Correspondence to Xiang Li.

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Liu, H., Li, X. & Gong, W. Rethinking the differential evolution algorithm. SOCA 14, 79–87 (2020). https://doi.org/10.1007/s11761-020-00286-x

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  • DOI: https://doi.org/10.1007/s11761-020-00286-x

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