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Grey wolf optimizer: a review of recent variants and applications

Neural computing & applications, 2018-07, Vol.30 (2), p.413-435 [Peer Reviewed Journal]

The Natural Computing Applications Forum 2017 ;Copyright Springer Science & Business Media 2018 ;ISSN: 0941-0643 ;EISSN: 1433-3058 ;DOI: 10.1007/s00521-017-3272-5

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  • Title:
    Grey wolf optimizer: a review of recent variants and applications
  • Author: Faris, Hossam ; Aljarah, Ibrahim ; Al-Betar, Mohammed Azmi ; Mirjalili, Seyedali
  • Subjects: Artificial Intelligence ; Bioinformatics ; Computational Biology/Bioinformatics ; Computational Science and Engineering ; Computer Science ; Data Mining and Knowledge Discovery ; Global optimization ; Image processing ; Image Processing and Computer Vision ; Machine learning ; Probability and Statistics in Computer Science ; Review ; Swarm intelligence
  • Is Part Of: Neural computing & applications, 2018-07, Vol.30 (2), p.413-435
  • Description: 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.
  • Publisher: London: Springer London
  • Language: English
  • Identifier: ISSN: 0941-0643
    EISSN: 1433-3058
    DOI: 10.1007/s00521-017-3272-5
  • Source: AUTh Library subscriptions: ProQuest Central
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