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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2020: Intelligent Data Engineering and Automated Learning – IDEAL 2020 pp 329–337Cite as

Entropy Based Grey Wolf Optimizer

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12489))

Abstract

Recently Shannon’s Entropy has been incorporated in nature inspired metaheuristics with good results. Depending on the problem, the Grey Wolf Optimization (GWO) algorithm may suffer from premature convergence. Here, an Entropy Grey Wolf Optimization (E-GWO) technique is proposed with the overall aim to improve the original GWO performance. The entropy is used to track the GWO swarm diversity, comparing the distance values between the Alpha in relation to the Beta and Delta wolves. The aim of the E-GWO variant is to improve convergence and prevent stagnation in local optima, since ideally restarting the swarm agents will prevent this from happening. Simulation results are presented showing that E-GWO restarting mechanism can achieve better results than the original GWO algorithm for some benchmark functions.

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Author information

Authors and Affiliations

  1. Department of Engineering, University of Trás-os-Montes and Alto Douro (UTAD), Vila Real, Portugal

    Daniel Duarte, P. B. de Moura Oliveira & E. J. Solteiro Pires

  2. INESC-TEC Technology and Science, Campus da FEUP, Porto, Portugal

    P. B. de Moura Oliveira & E. J. Solteiro Pires

Authors
  1. Daniel Duarte
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  2. P. B. de Moura Oliveira
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  3. E. J. Solteiro Pires
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Corresponding author

Correspondence to P. B. de Moura Oliveira .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    Cesar Analide

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. Technical University of Madrid, Madrid, Spain

    David Camacho

  4. University of Manchester, Manchester, UK

    Hujun Yin

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Duarte, D., de Moura Oliveira, P.B., Solteiro Pires, E.J. (2020). Entropy Based Grey Wolf Optimizer. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_29

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  • DOI: https://doi.org/10.1007/978-3-030-62362-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62361-6

  • Online ISBN: 978-3-030-62362-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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