A comprehensive survey of honey badger optimization algorithm and meta-analysis of its variants and applications

Abstract

Metaheuristic algorithms are commonly used in solving complex and NP-hard optimization problems in various fields. These algorithms have become popular because of their ability to explore and exploit solutions in various problem domains. Honey Badger Algorithm (HBA) is a population-based metaheuristic optimization algorithm inspired by the dynamic hunting strategy of honey badgers, utilizing honey and digging-seeking techniques. Since its introduction in 2020, HBA has garnered widespread attention and has been applied across various domains. This review aims to comprehensively survey the improvement and application of HBA in solving various optimization problems. Additionally, the survey conducts a meta-analysis of the HBA's improvements, hybridization and application since its introduction. According to the result of the survey, 52 studies presented improved HBA using chaotic maps, levy flight mechanism, adaptive mechanisms, transfer functions, multi-objective mechanism and opposition based learning techniques, 20 studies presented a hybrid HBA with other metaheuristics algorithms and 101 studies uses the original HBA for solving various optimization problems. According to the survey, the wide acceptance of the HBA within the research community stems from its straightforwardness, ease of use, efficient computational time, accelerated convergence speed, high efficacy, and capability to address different kind of optimization issues, distinguishing it from the well-known optimization approches presented.