A Comprehensive Survey of Henry Gas Solubility Optimization Algorithm with its Theory, Variants, and Applications

Henry Gas Solubility Optimization (HGSO) algorithm is a well-known physics-based nature-inspired optimization algorithm inspired by the behavior of Henry’s law. The HGSO algorithm, developed by Hashim et al. in 2019, has attracted significant interest from scientists and researchers. It has been widely applied to solve various optimization problems in different fields due to its unique structure, simplicity, easiness of implementation, and reasonable execution time. This paper explores and examines over 200 previous existing research on the HGSO algorithm covering its advancements, enhanced variants (multi-objective, hybridized, and modified), and a wide range of real-world applications such as intrusion detection, wireless sensor networks, optimal parameters control, photovoltaic systems, image processing, and feature selection. Additionally, The performance of the HGSO algorithm is assessed using 23 IEEE CEC benchmark functions in comparison with 14 well-regarded optimization meta-heuristics published in the literature. Furthermore, the results of the HGSO algorithm are compared with some of its key variants. The survey also provides a critical evaluation of HGSO’s convergence behavior, highlighting its strengths and limitations. Finally, the paper concludes with some potential directions for future work. The insights gained from this survey offer valuable guidance for researchers aiming to apply or enhance the HGSO algorithm in a wide range of optimization problems.