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Dynamic Configuration of Differential Evolution Control Parameters and Operators

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Artificial Life and Computational Intelligence (ACALCI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9592))

Abstract

Differential evolution has shown success in solving different optimization problems. However, its performance depends on the control parameters and search operators. Different from existing approaches, in this paper, a new framework which dynamically configures the appropriate choices of operators and parameters is introduced, in which the success of a search operator is linked to the proper combination of control parameters (scaling factor and crossover rate). Also, an adaptation of the population size is adopted. The performance of the proposed algorithm is assessed using a well-known set of constrained problems with the experimental results demonstrating that it is superior to state-of-the-art algorithms.

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Acknowledgment

This work was supported by an Australian Research Council Discovery Project (Grant# DP150102583) awarded to A/Prof. Ruhul Sarker.

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Authors and Affiliations

  1. School of Engineering and Information Technology, University of New South Wales at Canberra, Canberra, Australia

    Saber Mohammed Elsayed & Ruhul Sarker

Authors
  1. Saber Mohammed Elsayed
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  2. Ruhul Sarker
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Corresponding author

Correspondence to Saber Mohammed Elsayed .

Editor information

Editors and Affiliations

  1. Canberra, Aust Capital Terr, Australia

    Tapabrata Ray

  2. University of New South Wales, Canberra, Aust Capital Terr, Australia

    Ruhul Sarker

  3. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

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Elsayed, S.M., Sarker, R. (2016). Dynamic Configuration of Differential Evolution Control Parameters and Operators . In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-28270-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28269-5

  • Online ISBN: 978-3-319-28270-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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