Whale optimization algorithm - comprehensive meta analysis on hybridization, latest improvements, variants and applications for complex optimization problems

Whale Optimization Algorithm (WOA) is an emerging metaheuristics that uses humpback whale's bubble-net hunting scheme to solve challenging optimization problems. Due to its easy to implement structure, low operator requirement, faster convergence speed, and improved ability to balance the diversification and intensification tradeoffs, this swarm intelligence technique has gained widespread acceptance in a variety of engineering domains. The application, modification, and hybridization of WOA in many technical fields are further explored in this review. The description of the advantages, lacunas in the existing methods of various variants of WOA, and future research support opportunities are investigated. The systematic literature review serves as a means to extract the lacunas of recent developments on WOA. The WOA is briefly reviewed in this study with an emphasis on its latest versions and applications to single-objective and multi-objective optimization tasks with complicated search space. Dedicated to the increasing intricacy of computational models and the requirement for speedy engineering decisions, research attention has been given to WOA for decision-making. This survey also focuses on providing an updated review of WOA with respect to hybridization, improvements and variants. In-depth information about WOA's algorithmic origins, characteristics, restrictions, and applications are also provided. Research on optimization performance enhancements based on WOA is anticipated to grow in future. This review has the potential to spur experienced researchers to suggest more developments on WOA that can be used as a primary reference for researchers.