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Differential Evolution with Landscape-Based Operator Selection for Solving Numerical Optimization Problems

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Intelligent and Evolutionary Systems

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 8))

Abstract

In this paper, a new differential evolution framework is proposed. In it, the best-performing differential evolution mutation strategy, from a given set, is dynamically determined based on a problem’s landscape, as well as the performance history of each operator. The performance of the proposed algorithm has been tested on a set of 30 unconstrained single objective real-parameter optimization problems. The experimental results show that the proposed algorithm is capable of producing good solutions that are clearly better than those obtained from a set of considered state-of-the-art algorithms.

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Author information

Authors and Affiliations

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

    Karam M. Sallam, Saber M. Elsayed, Ruhul A. Sarker & Daryl L. Essam

Authors
  1. Karam M. Sallam
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  2. Saber M. Elsayed
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  3. Ruhul A. Sarker
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  4. Daryl L. Essam
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Corresponding author

Correspondence to Karam M. Sallam .

Editor information

Editors and Affiliations

  1. School of Engineering and Information Technology, Australian Defence Force Academy, The University of New South Wales, Canberra, Australian Capital Territory, Australia

    George Leu

  2. School of Engineering and Information Technology, Australian Defence Force Academy, The University of New South Wales, Canberra, Australian Capital Territory, Australia

    Hemant Kumar Singh

  3. School of Engineering and Information Technology, Australian Defence Force Academy, The University of New South Wales, Canberra, Australian Capital Territory, Australia

    Saber Elsayed

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Sallam, K.M., Elsayed, S.M., Sarker, R.A., Essam, D.L. (2017). Differential Evolution with Landscape-Based Operator Selection for Solving Numerical Optimization Problems. In: Leu, G., Singh, H., Elsayed, S. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-49049-6_27

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  • DOI: https://doi.org/10.1007/978-3-319-49049-6_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49048-9

  • Online ISBN: 978-3-319-49049-6

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