Journal ArticleDOI
Grey wolf optimizer: a review of recent variants and applications
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TLDR
In this review paper, several research publications using GWO have been overviewed and summarized and the main foundation of GWO is provided, which suggests several possible future directions that can be further investigated.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.read more
Citations
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Journal ArticleDOI
An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems
Hossam Faris,Majdi Mafarja,Ali Asghar Heidari,Ibrahim Aljarah,Ala' M. Al-Zoubi,Seyedali Mirjalili,Hamido Fujita +6 more
TL;DR: Two new wrapper FS approaches that use SSA as the search strategy are proposed and it is observed that the proposed approach significantly outperforms others on around 90% of the datasets.
Journal ArticleDOI
An improved grey wolf optimizer for solving engineering problems
TL;DR: The experimental results and statistical tests demonstrate that the I-GWO algorithm is very competitive and often superior compared to the algorithms used in the experiments, and the results of the proposed algorithm on the engineering design problems demonstrate its efficiency and applicability.
Journal ArticleDOI
Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems
Majdi Mafarja,Ibrahim Aljarah,Ali Asghar Heidari,Abdelaziz I. Hammouri,Hossam Faris,Ala' M. Al-Zoubi,Seyedali Mirjalili +6 more
TL;DR: The comprehensive results and various comparisons reveal that the EPD has a remarkable impact on the efficacy of the GOA and using the selection mechanism enhanced the capability of the proposed approach to outperform other optimizers and find the best solutions with improved convergence trends.
Journal ArticleDOI
Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection
TL;DR: A binary version of the hybrid grey wolf optimization (GWO) and particle swarm optimization (PSO) is proposed to solve feature selection problems in this paper and significantly outperformed the binary GWO (BGWO), the binary PSO, the binary genetic algorithm, and the whale optimization algorithm with simulated annealing when using several performance measures.
Journal ArticleDOI
A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection
Mohamed Abdel-Basset,Doaa El-Shahat,Ibrahim El-Henawy,Victor Hugo C. de Albuquerque,Seyedali Mirjalili +4 more
TL;DR: A new Grey Wolf Optimizer algorithm integrated with a Two-phase Mutation to solve the feature selection for classification problems based on the wrapper methods to reduce the number of selected features while preserving high classification accuracy.
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Journal ArticleDOI
Grey Wolf Optimizer
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.