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This paper discusses a new variant of the Henry Gas Solubility Optimization(HGSO) Algorithm, called Hybrid HGSO (HHGSO). Unlike its predecessor, HHGSOallows multiple clusters serving different individual meta-heuristic algorithms(i.e., with its own defined parameters and local best) to coexist within thesame population. Exploiting the dynamic cluster-to-algorithm mapping viapenalized and reward model with adaptive switching factor, HHGSO offers a novelapproach for meta-heuristic hybridization consisting of Jaya Algorithm, SootyTern Optimization Algorithm, Butterfly Optimization Algorithm, and Owl SearchAlgorithm, respectively. The acquired results from the selected two casestudies (i.e., involving team formation problem and combinatorial test suitegeneration) indicate that the hybridization has notably improved theperformance of HGSO and gives superior performance against other competingmeta-heuristic and hyper-heuristic algorithms.

Hybrid Henry Gas Solubility Optimization Algorithm with Dynamic Cluster-to-Algorithm Mapping for Search-based Software Engineering Problems