A multi-objective hyper-heuristic based on choice function
Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an attempt to solve difficult computational optimization problems. We present a learning selection choice function based hyper-heuristic to solve multi-objective optimization problems. This high level a...
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| Format: | Article |
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Elsevier
2014
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| Online Access: | https://eprints.nottingham.ac.uk/32175/ |
| _version_ | 1848794349926612992 |
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| author | Maashi, Mashael Özcan, Ender Kendall, Graham |
| author_facet | Maashi, Mashael Özcan, Ender Kendall, Graham |
| author_sort | Maashi, Mashael |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an attempt to solve difficult computational optimization problems. We present a learning selection choice function based hyper-heuristic to solve multi-objective optimization problems. This high level approach controls and combines the strengths of three well-known multi-objective evolutionary algorithms (i.e. NSGAII, SPEA2 and MOGA), utilizing them as the low level heuristics. The performance of the proposed learning hyper-heuristic is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, the proposed hyper-heuristic is applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the hyper-heuristic approach when compared to the performance of each low level heuristic run on its own, as well as being compared to other approaches including an adaptive multi-method search, namely AMALGAM. |
| first_indexed | 2025-11-14T19:14:47Z |
| format | Article |
| id | nottingham-32175 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:14:47Z |
| publishDate | 2014 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-321752020-05-04T20:14:00Z https://eprints.nottingham.ac.uk/32175/ A multi-objective hyper-heuristic based on choice function Maashi, Mashael Özcan, Ender Kendall, Graham Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an attempt to solve difficult computational optimization problems. We present a learning selection choice function based hyper-heuristic to solve multi-objective optimization problems. This high level approach controls and combines the strengths of three well-known multi-objective evolutionary algorithms (i.e. NSGAII, SPEA2 and MOGA), utilizing them as the low level heuristics. The performance of the proposed learning hyper-heuristic is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, the proposed hyper-heuristic is applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the hyper-heuristic approach when compared to the performance of each low level heuristic run on its own, as well as being compared to other approaches including an adaptive multi-method search, namely AMALGAM. Elsevier 2014-07 Article PeerReviewed Maashi, Mashael, Özcan, Ender and Kendall, Graham (2014) A multi-objective hyper-heuristic based on choice function. Expert Systems with Applications, 41 (9). pp. 4475-4493. ISSN 0957-4174 Hyper-heuristic; Metaheuristic; Evolutionary algorithm; Multi-objective optimization http://www.sciencedirect.com/science/article/pii/S095741741400013X doi:10.1016/j.eswa.2013.12.050 doi:10.1016/j.eswa.2013.12.050 |
| spellingShingle | Hyper-heuristic; Metaheuristic; Evolutionary algorithm; Multi-objective optimization Maashi, Mashael Özcan, Ender Kendall, Graham A multi-objective hyper-heuristic based on choice function |
| title | A multi-objective hyper-heuristic based on choice function |
| title_full | A multi-objective hyper-heuristic based on choice function |
| title_fullStr | A multi-objective hyper-heuristic based on choice function |
| title_full_unstemmed | A multi-objective hyper-heuristic based on choice function |
| title_short | A multi-objective hyper-heuristic based on choice function |
| title_sort | multi-objective hyper-heuristic based on choice function |
| topic | Hyper-heuristic; Metaheuristic; Evolutionary algorithm; Multi-objective optimization |
| url | https://eprints.nottingham.ac.uk/32175/ https://eprints.nottingham.ac.uk/32175/ https://eprints.nottingham.ac.uk/32175/ |