Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

The Henry Gas Solubility Optimization (HGSO) is a physics-based metaheuristic inspired by Henry's law, which describes the solubility of the gas in a liquid under specific pressure conditions. Since its introduction by Hashim et al. in 2019, HGSO has gained significant attention for its unique...

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Vydáno v:IEEE access Ročník 12; s. 26062 - 26095
Hlavní autoři: El-Shorbagy, Mohammed A., Bouaouda, Anas, Nabwey, Hossam A., Abualigah, Laith, Hashim, Fatma A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Behavioral sciences
Engineering - general
Engineering problems
Gas solubility
global optimization
Henry gas solubility optimization
Heuristic algorithms
Heuristic methods
Mathematical models
metaheuristic algorithm
Metaheuristics
Optimization
Physics
Problem-solving
Search problems
Space exploration
Task analysis
ISSN:2169-3536, 2169-3536
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Shrnutí:The Henry Gas Solubility Optimization (HGSO) is a physics-based metaheuristic inspired by Henry's law, which describes the solubility of the gas in a liquid under specific pressure conditions. Since its introduction by Hashim et al. in 2019, HGSO has gained significant attention for its unique features, including minimal adaptive parameters and a balanced exploration-exploitation trade-off, leading to favorable convergence. This study provides an up-to-date survey of HGSO, covering the walk through the historical development of HGSO, its modifications, and hybridizations with other algorithms, showcasing its adaptability and potential for synergy. Recent variants of HGSO are categorized into modified, hybridized, and multi-objective versions, and the review explores its main applications, demonstrating its effectiveness in solving complex problems. The evaluation includes a discussion of the algorithm's strengths and weaknesses. This comprehensive review, featuring graphical and tabular comparisons, not only indicates potential future directions in the field but also serves as a valuable resource for researchers seeking a deep understanding of HGSO and its advanced versions. As physics-based metaheuristic algorithms gain prominence for solving intricate optimization problems, this study provides insights into the adaptability and applications of HGSO across diverse domains.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3365700