Choice function based hyper-heuristics for multi-objective optimization
A selection hyper-heuristic is a high level search methodology which operates over a fixed set of low level heuristics. During the iterative search process, a heuristic is selected and applied to a candidate solution in hand, producing a new solution which is then accepted or rejected at each step....
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| Format: | Article |
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Elsevier
2015
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| Online Access: | https://eprints.nottingham.ac.uk/31171/ |
| _version_ | 1848794141641670656 |
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| author | Özcan, Ender |
| author_facet | Özcan, Ender |
| author_sort | Özcan, Ender |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | A selection hyper-heuristic is a high level search methodology which operates over a fixed set of low level heuristics. During the iterative search process, a heuristic is selected and applied to a candidate solution in hand, producing a new solution which is then accepted or rejected at each step. Selection hyper-heuristics have been increasingly, and successfully, applied to single-objective optimization problems, while work on multi-objective selection hyper-heuristics is limited. This work presents one of the initial studies on selection hyper-heuristics combining a choice function heuristic selection methodology with great deluge and late acceptance as non-deterministic move acceptance methods for multi-objective optimization. A well-known hypervolume metric is integrated into the move acceptance methods to enable the approaches to deal with multi-objective problems. The performance of the proposed hyper-heuristics is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, they are applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the non-deterministic move acceptance, particularly great deluge when used as a component of a choice function based selection hyper-heuristic. |
| first_indexed | 2025-11-14T19:11:29Z |
| format | Article |
| id | nottingham-31171 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:11:29Z |
| publishDate | 2015 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-311712020-05-04T20:09:53Z https://eprints.nottingham.ac.uk/31171/ Choice function based hyper-heuristics for multi-objective optimization Özcan, Ender A selection hyper-heuristic is a high level search methodology which operates over a fixed set of low level heuristics. During the iterative search process, a heuristic is selected and applied to a candidate solution in hand, producing a new solution which is then accepted or rejected at each step. Selection hyper-heuristics have been increasingly, and successfully, applied to single-objective optimization problems, while work on multi-objective selection hyper-heuristics is limited. This work presents one of the initial studies on selection hyper-heuristics combining a choice function heuristic selection methodology with great deluge and late acceptance as non-deterministic move acceptance methods for multi-objective optimization. A well-known hypervolume metric is integrated into the move acceptance methods to enable the approaches to deal with multi-objective problems. The performance of the proposed hyper-heuristics is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, they are applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the non-deterministic move acceptance, particularly great deluge when used as a component of a choice function based selection hyper-heuristic. Elsevier 2015-03 Article PeerReviewed Özcan, Ender (2015) Choice function based hyper-heuristics for multi-objective optimization. Applied Soft Computing, 28 . pp. 312-326. ISSN 1872-9681 Hyper-heuristic; Metaheuristic; Great deluge; Late acceptance; Multi-objective optimization http://www.sciencedirect.com/science/article/pii/S1568494614006449 doi:10.1016/j.asoc.2014.12.012 doi:10.1016/j.asoc.2014.12.012 |
| spellingShingle | Hyper-heuristic; Metaheuristic; Great deluge; Late acceptance; Multi-objective optimization Özcan, Ender Choice function based hyper-heuristics for multi-objective optimization |
| title | Choice function based hyper-heuristics for multi-objective optimization |
| title_full | Choice function based hyper-heuristics for multi-objective optimization |
| title_fullStr | Choice function based hyper-heuristics for multi-objective optimization |
| title_full_unstemmed | Choice function based hyper-heuristics for multi-objective optimization |
| title_short | Choice function based hyper-heuristics for multi-objective optimization |
| title_sort | choice function based hyper-heuristics for multi-objective optimization |
| topic | Hyper-heuristic; Metaheuristic; Great deluge; Late acceptance; Multi-objective optimization |
| url | https://eprints.nottingham.ac.uk/31171/ https://eprints.nottingham.ac.uk/31171/ https://eprints.nottingham.ac.uk/31171/ |