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

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Abstract

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.

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Acknowledgements

The work reported in this paper is funded by the Malaysian Technical University Network (MTUN) Research Grant from the Ministry of Higher Education Malaysia titled: The Development of T-Way Test Generation Tool for Combinatorial Testing (Grant No: UIC19102). Bestoun S. Ahmed supported by the Knowledge Foundation of Sweden (KKS) through the Synergy Project AIDA - A Holistic AI-driven Networking and Processing Framework for Industrial IoT (Rek:20200067).

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  1. Faculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang, 26600, Pekan, Pahang, Malaysia

    Kamal Z. Zamli, Md. Abdul Kader & Saiful Azad

  2. Department of Mathematics and Computer Science, Karlstad University, 65188, Karlstad, Sweden

    Bestoun S. Ahmed

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Zamli, K.Z., Kader, M.A., Azad, S. et al. Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping. Neural Comput & Applic 33, 8389–8416 (2021). https://doi.org/10.1007/s00521-020-05594-z

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