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Grey wolf optimizer: a review of recent variants and applications

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Abstract

Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated.

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Authors and Affiliations

  1. Business Information Technology Department, King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Hossam Faris & Ibrahim Aljarah

  2. Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Irbid, Jordan

    Mohammed Azmi Al-Betar

  3. Institute for Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD, 4111, Australia

    Seyedali Mirjalili

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  1. Hossam Faris
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  2. Ibrahim Aljarah
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  3. Mohammed Azmi Al-Betar
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  4. Seyedali Mirjalili
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Correspondence to Seyedali Mirjalili.

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Faris, H., Aljarah, I., Al-Betar, M.A. et al. Grey wolf optimizer: a review of recent variants and applications. Neural Comput & Applic 30, 413–435 (2018). https://doi.org/10.1007/s00521-017-3272-5

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