On the exploration and exploitation in popular swarm-based metaheuristic algorithms
It is obvious from wider spectrum of successful applications that metaheuristic algorithms are potential solutions to hard optimization problems. Among such algorithms are swarm-based methods like particle swarm optimization and ant colony optimization which are increasingly attracting new researche...
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| Format: | Article |
| Language: | en |
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2018
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| Online Access: | http://eprints.uthm.edu.my/3465/1/AJ%202018%20%28348%29.pdf http://eprints.uthm.edu.my/3465/ https://doi.org/10.1007/s00521-018-3592-0 |
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| author | Hussain, Kashif Mohd Salleh, Mohd Najib Cheng, Shi Shi, Yuhui |
| author_facet | Hussain, Kashif Mohd Salleh, Mohd Najib Cheng, Shi Shi, Yuhui |
| author_sort | Hussain, Kashif |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | It is obvious from wider spectrum of successful applications that metaheuristic algorithms are potential solutions to hard optimization problems. Among such algorithms are swarm-based methods like particle swarm optimization and ant colony optimization which are increasingly attracting new researchers. Despite popularity, the core questions on performance issues are still partially answered due to limited insightful analyses. Mere investigation and comparison of end results may not reveal the reasons behind poor or better performance. This study, therefore, performed in-depth empirical analysis by quantitatively analyzing exploration and exploitation of five swarm-based metaheuristic algorithms. The analysis unearthed explanations the way algorithms performed on numerical problems as well as on real-world application of classification using adaptive neuro-fuzzy inference system (ANFIS) trained by selected metaheuristics. The outcome of empirical study suggested that coherence and consistency in the swarm individuals throughout iterations is the key to success in swarmbased metaheuristic algorithms. The analytical approach adopted in this study may be employed to perform componentwise diversity analysis so that the contribution of each component on performance may be determined for devising efficient search strategies. |
| format | Article |
| id | my.uthm.eprints-3465 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2018 |
| publisher | Springer |
| record_format | eprints |
| spelling | my.uthm.eprints-34652021-11-17T07:38:34Z http://eprints.uthm.edu.my/3465/ On the exploration and exploitation in popular swarm-based metaheuristic algorithms Hussain, Kashif Mohd Salleh, Mohd Najib Cheng, Shi Shi, Yuhui Q Science (General) It is obvious from wider spectrum of successful applications that metaheuristic algorithms are potential solutions to hard optimization problems. Among such algorithms are swarm-based methods like particle swarm optimization and ant colony optimization which are increasingly attracting new researchers. Despite popularity, the core questions on performance issues are still partially answered due to limited insightful analyses. Mere investigation and comparison of end results may not reveal the reasons behind poor or better performance. This study, therefore, performed in-depth empirical analysis by quantitatively analyzing exploration and exploitation of five swarm-based metaheuristic algorithms. The analysis unearthed explanations the way algorithms performed on numerical problems as well as on real-world application of classification using adaptive neuro-fuzzy inference system (ANFIS) trained by selected metaheuristics. The outcome of empirical study suggested that coherence and consistency in the swarm individuals throughout iterations is the key to success in swarmbased metaheuristic algorithms. The analytical approach adopted in this study may be employed to perform componentwise diversity analysis so that the contribution of each component on performance may be determined for devising efficient search strategies. Springer 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/3465/1/AJ%202018%20%28348%29.pdf Hussain, Kashif and Mohd Salleh, Mohd Najib and Cheng, Shi and Shi, Yuhui (2018) On the exploration and exploitation in popular swarm-based metaheuristic algorithms. Neural Computing and Applications, 13 (3). pp. 7665-7683. ISSN 0941-0643 https://doi.org/10.1007/s00521-018-3592-0 |
| spellingShingle | Q Science (General) Hussain, Kashif Mohd Salleh, Mohd Najib Cheng, Shi Shi, Yuhui On the exploration and exploitation in popular swarm-based metaheuristic algorithms |
| title | On the exploration and exploitation in popular swarm-based
metaheuristic algorithms |
| title_full | On the exploration and exploitation in popular swarm-based
metaheuristic algorithms |
| title_fullStr | On the exploration and exploitation in popular swarm-based
metaheuristic algorithms |
| title_full_unstemmed | On the exploration and exploitation in popular swarm-based
metaheuristic algorithms |
| title_short | On the exploration and exploitation in popular swarm-based
metaheuristic algorithms |
| title_sort | on the exploration and exploitation in popular swarm-based
metaheuristic algorithms |
| topic | Q Science (General) |
| url | http://eprints.uthm.edu.my/3465/1/AJ%202018%20%28348%29.pdf http://eprints.uthm.edu.my/3465/ https://doi.org/10.1007/s00521-018-3592-0 |
| url_provider | http://eprints.uthm.edu.my/ |