Grey wolf optimizer: a review of recent variants and applications

By Hossam Faris, Ibrahim Aljarah, Mohammed Azmi Al-Betar, Seyedali Mirjalili. Neural computing & applications (Print), 2017

1,033 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/b9fc5e9b6dc24380c788d2b2c2f7d7a4cd8943c4

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Citations in the corpus

An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems (2018, methodology): “The GWO is a successful SI algorithm that mimics the social hierarchy and hunting traits of the grey wolves [21, 43, 44, 45].”
Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection (2019, background): “This algorithm mimics the hunting and dominancy behaviour of grey wolves in nature [23].”