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

• Published in: Neural computing & applications Vol. 33; no. 14; pp. 8389 - 8416
• Main Authors: Zamli, Kamal Z., Kader, Md. Abdul, Azad, Saiful, Ahmed, Bestoun S.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2021; Springer Nature B.V
• Subjects: Adaptive switching; Artificial Intelligence; Clustering algorithms; Clusters; Combinatorial analysis; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Datavetenskap; Dynamic cluster; Gas solubility; Henry Gas Solubility Optimization Algorithm; Heuristic; Heuristic algorithms; Heuristic methods; Hybrid meta-heuristic algorithm; Hyper-heuristic algorithms; Image Processing and Computer Vision; Mapping; Meta heuristic algorithm; Multiple clusters; Optimization; Optimization algorithms; Original Article; Probability and Statistics in Computer Science; Search Algorithms; Search-based Software Engineering; Solubility; Team formation; Henry Gas Solubility Optimization Algorithm; Search-based Software Engineering; Hybrid meta-heuristic algorithm
• ISSN: 0941-0643; 1433-3058; 1433-3058
• DOI: 10.1007/s00521-020-05594-z

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.