A learning automata based multiobjective hyper-heuristic
Metaheuristics, being tailored to each particular domain by experts, have been successfully applied to many computationally hard optimisation problems. However, once implemented, their application to a new problem domain or a slight change in the problem description would often require additional ex...
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| Format: | Article |
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IEEE
2017
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| Online Access: | https://eprints.nottingham.ac.uk/48692/ |
| _version_ | 1848797824036110336 |
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| author | Li, Wenwen Özcan, Ender John, Robert |
| author_facet | Li, Wenwen Özcan, Ender John, Robert |
| author_sort | Li, Wenwen |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Metaheuristics, being tailored to each particular domain by experts, have been successfully applied to many computationally hard optimisation problems. However, once implemented, their application to a new problem domain or a slight change in the problem description would often require additional expert intervention. There is a growing number of studies on reusable cross-domain search methodologies, such as, selection hyper-heuristics, which are applicable to problem instances from various domains, requiring minimal expert intervention or even none. This study introduces a new learning automata based selection hyper-heuristic controlling a set of multiobjective metaheuristics. The approach operates above three well-known multiobjective evolutionary algorithms and mixes them, exploiting the strengths of each algorithm. The performance and behaviour of two variants of the proposed selection hyper-heuristic, each utilising a different initialisation scheme are investigated across a range of unconstrained multiobjective mathematical benchmark functions from two different sets and the realworld problem of vehicle crashworthiness. The empirical results illustrate the effectiveness of our approach for cross-domain search, regardless of the initialisation scheme, on those problems when compared to each individual multiobjective algorithm. Moreover, both variants perform signicantly better than some previously proposed selection hyper-heuristics for multiobjective optimisation, thus signicantly enhancing the opportunities for improved multiobjective optimisation. |
| first_indexed | 2025-11-14T20:10:00Z |
| format | Article |
| id | nottingham-48692 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:10:00Z |
| publishDate | 2017 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-486922020-05-04T19:23:19Z https://eprints.nottingham.ac.uk/48692/ A learning automata based multiobjective hyper-heuristic Li, Wenwen Özcan, Ender John, Robert Metaheuristics, being tailored to each particular domain by experts, have been successfully applied to many computationally hard optimisation problems. However, once implemented, their application to a new problem domain or a slight change in the problem description would often require additional expert intervention. There is a growing number of studies on reusable cross-domain search methodologies, such as, selection hyper-heuristics, which are applicable to problem instances from various domains, requiring minimal expert intervention or even none. This study introduces a new learning automata based selection hyper-heuristic controlling a set of multiobjective metaheuristics. The approach operates above three well-known multiobjective evolutionary algorithms and mixes them, exploiting the strengths of each algorithm. The performance and behaviour of two variants of the proposed selection hyper-heuristic, each utilising a different initialisation scheme are investigated across a range of unconstrained multiobjective mathematical benchmark functions from two different sets and the realworld problem of vehicle crashworthiness. The empirical results illustrate the effectiveness of our approach for cross-domain search, regardless of the initialisation scheme, on those problems when compared to each individual multiobjective algorithm. Moreover, both variants perform signicantly better than some previously proposed selection hyper-heuristics for multiobjective optimisation, thus signicantly enhancing the opportunities for improved multiobjective optimisation. IEEE 2017-12-20 Article PeerReviewed Li, Wenwen, Özcan, Ender and John, Robert (2017) A learning automata based multiobjective hyper-heuristic. IEEE Transactions on Evolutionary Computation . ISSN 1089-778X Online learning Multiobjective optimisation Hyper-heuristics Evolutionary algorithms Operational research http://ieeexplore.ieee.org/document/8231198/ doi:10.1109/TEVC.2017.2785346 doi:10.1109/TEVC.2017.2785346 |
| spellingShingle | Online learning Multiobjective optimisation Hyper-heuristics Evolutionary algorithms Operational research Li, Wenwen Özcan, Ender John, Robert A learning automata based multiobjective hyper-heuristic |
| title | A learning automata based multiobjective hyper-heuristic |
| title_full | A learning automata based multiobjective hyper-heuristic |
| title_fullStr | A learning automata based multiobjective hyper-heuristic |
| title_full_unstemmed | A learning automata based multiobjective hyper-heuristic |
| title_short | A learning automata based multiobjective hyper-heuristic |
| title_sort | learning automata based multiobjective hyper-heuristic |
| topic | Online learning Multiobjective optimisation Hyper-heuristics Evolutionary algorithms Operational research |
| url | https://eprints.nottingham.ac.uk/48692/ https://eprints.nottingham.ac.uk/48692/ https://eprints.nottingham.ac.uk/48692/ |