  This paper discusses a new variant of the 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.
