Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping
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...
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my.ump.umpir.339742022-05-09T03:43:09Z http://umpir.ump.edu.my/id/eprint/33974/ Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping Kamal Z., Zamli Kader, Md. Abdul Azad, Saiful Ahmed, Bestoun S. QA76 Computer software 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. Springer 2021 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/33974/1/Hybrid%20Henry%20gas%20solubility.pdf 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 https://doi.org/10.1007/s00521-020-05594-z https://doi.org/10.1007/s00521-020-05594-z |
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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. |
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Article |
author |
Kamal Z., Zamli Kader, Md. Abdul Azad, Saiful Ahmed, Bestoun S. |
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Kamal Z., Zamli Kader, Md. Abdul Azad, Saiful Ahmed, Bestoun S. |
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Kamal Z., Zamli |
title |
Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping |
title_short |
Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping |
title_full |
Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping |
title_fullStr |
Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping |
title_full_unstemmed |
Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping |
title_sort |
hybrid henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping |
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Springer |
publishDate |
2021 |
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http://umpir.ump.edu.my/id/eprint/33974/1/Hybrid%20Henry%20gas%20solubility.pdf http://umpir.ump.edu.my/id/eprint/33974/ https://doi.org/10.1007/s00521-020-05594-z https://doi.org/10.1007/s00521-020-05594-z |
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