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Nature-Inspired Optimizers pp 87–105Cite as

Grey Wolf Optimizer: Theory, Literature Review, and Application in Computational Fluid Dynamics Problems

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 811))

Abstract

This chapter first discusses inspirations, methematicam models, and an in-depth literature of the recently proposed Grey Wolf Optimizer (GWO). Then, several experiments are conducted to analyze and benchmark the performance of different variants and improvements of this algorithm. The chapter also investigates the application of the GWO variants in finding an optimal design for a ship propeller.

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Author information

Authors and Affiliations

  1. Institute of Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD, 4111, Australia

    Seyedali Mirjalili

  2. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Ibrahim Aljarah & Hossam Faris

  3. Department of Computer Science, Faculty of Engineering and Technology, Birzeit University, PoBox 14, Birzeit, Palestine

    Majdi Mafarja

  4. School of Surveying and Geospatial Engineering, University of Tehran, Tehran, Iran

    Ali Asghar Heidari

Authors
  1. Seyedali Mirjalili
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  2. Ibrahim Aljarah
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  3. Majdi Mafarja
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  4. Ali Asghar Heidari
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  5. Hossam Faris
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Corresponding author

Correspondence to Seyedali Mirjalili .

Editor information

Editors and Affiliations

  1. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Seyedali Mirjalili

  2. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Jin Song Dong

  3. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Andrew Lewis

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Mirjalili, S., Aljarah, I., Mafarja, M., Heidari, A.A., Faris, H. (2020). Grey Wolf Optimizer: Theory, Literature Review, and Application in Computational Fluid Dynamics Problems. In: Mirjalili, S., Song Dong, J., Lewis, A. (eds) Nature-Inspired Optimizers. Studies in Computational Intelligence, vol 811. Springer, Cham. https://doi.org/10.1007/978-3-030-12127-3_6

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