A systematic review of applying grey wolf optimizer, its variants, and its developments in different Internet of Things applications

Abstract

The Internet of Things (IoT) shapes an organization of objects that can interface and share information with different devices using sensors, computer programs, and other innovations without human intervention. IoT problems deal with massive amounts of data with critical challenges such as complex and dynamic search spaces, multiple objectives and constraints, uncertainty, and noise that require an efficient optimizer to extract valuable insights. Grey wolf optimizer (GWO) is an efficient optimizer stimulated by the hunting mechanism of wolves. The increasing trend of applying GWO shows that although it is a simple algorithm with few control parameters, it effectively solves optimization problems, particularly in various IoT applications. Therefore, this study reviews applying GWO, its variants, and its developments in IoT applications. This systematic review uses the PRISMA methodology, including three fundamental phases: identification, evaluation, and reporting. In the identification phase, the target search problems are defined based on suitable keywords and alternative synonyms, and then 693 documents from 2014 to the end of 2023 are retrieved. The evaluation phase applies three screening steps to assess papers and choose 50 eligible papers for full-text reading. Finally, the reporting phase thoroughly examines and synthesizes the 50 eligible articles to identify key themes related to GWOs in IoT applications. The eligible GWOs are reviewed in the development, commercial, consumer, and industrial categories. The paper visualized the spreading of eligible GWOs according to their publisher, application, journal, and country and then suggested future directions for research.