Grey wolf optimizer: a review of recent variants and applications

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 requ...

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Veröffentlicht in:Neural computing & applications Jg. 30; H. 2; S. 413 - 435
Hauptverfasser: Faris, Hossam, Aljarah, Ibrahim, Al-Betar, Mohammed Azmi, Mirjalili, Seyedali
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.07.2018
Springer Nature B.V
Schlagworte:
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
Metaheuristics
Optimization
GWO
ISSN:0941-0643, 1433-3058
Online-Zugang:Volltext
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Zusammenfassung: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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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-017-3272-5