Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping

Kamal Z., Zamli and Kader, Md. Abdul and Azad, Saiful and Ahmed, Bestoun S. (2021) Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping. Neural Computing and Applications, 33. pp. 8389-8416. ISSN 0941-0643. (Published)

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This paper discusses a new variant of Henry Gas Solubility Optimization (HGSO) Algorithm, called Hybrid HGSO (HHGSO). Unlike its predecessor, HHGSO allows multiple clusters serving different individual meta-heuristic algorithms (i.e., with its own defined parameters and local best) to coexist within the same population. Exploiting the dynamic cluster-to-algorithm mapping via penalized and reward model with adaptive switching factor, HHGSO offers a novel approach for meta-heuristic hybridization consisting of Jaya Algorithm, Sooty Tern Optimization Algorithm, Butterfly Optimization Algorithm, and Owl Search Algorithm, respectively. The acquired results from the selected two case studies (i.e., involving team formation problem and combinatorial test suite generation) indicate that the hybridization has notably improved the performance of HGSO and gives superior performance against other competing meta-heuristic and hyper-heuristic algorithms.

Item Type: Article
Uncontrolled Keywords: Hybrid meta-heuristic algorithm, Henry Gas Solubility Optimization Algorithm, Search-based Software Engineering
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computing
Depositing User: Noorul Farina Arifin
Date Deposited: 09 May 2022 03:43
Last Modified: 09 May 2022 03:43
URI: http://umpir.ump.edu.my/id/eprint/33974
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