Reinforcement Learning-Based Adaptive Operator Selection
| dc.authorid | Aydin, Mehmet/0000-0002-4890-5648 | |
| dc.contributor.author | Durgut, Rafet | |
| dc.contributor.author | Aydin, Mehmet Emin | |
| dc.date.accessioned | 2024-09-29T15:50:51Z | |
| dc.date.available | 2024-09-29T15:50:51Z | |
| dc.date.issued | 2021 | |
| dc.department | Karabük Üniversitesi | en_US |
| dc.description | 4th International Conference on Optimization and Learning (OLA) -- JUN 21-23, 2021 -- Catania, ITALY | en_US |
| dc.description.abstract | Metaheuristic and swarm intelligence approaches require devising optimisation algorithms with operators to let produce neighbouring solutions to conduct a move. The efficiency of algorithms using single operator remains recessive in comparison with those with multiple operators. However, use of multiple operators require a selection mechanism, which may not be always as productive as expected; therefore an adaptive selection scheme is always needed. In this study, an experience-based, reinforcement learning algorithm has been used to build an adaptive selection scheme implemented to work with a binary artificial bee colony algorithm in which the selection mechanism learns when and subject to which circumstances an operator can help produce better and worse neighbours. The implementations have been tested with commonly used benchmarks of uncapacitated facility location problem. The results demonstrates that the selection scheme developed based on reinforcement learning, which can also be named as smart selection scheme, performs much better that state-of-art adaptive selection schemes. | en_US |
| dc.identifier.doi | 10.1007/978-3-030-85672-4_3 | |
| dc.identifier.endpage | 41 | en_US |
| dc.identifier.isbn | 978-3-030-85671-7 | |
| dc.identifier.isbn | 978-3-030-85672-4 | |
| dc.identifier.issn | 1865-0929 | |
| dc.identifier.issn | 1865-0937 | |
| dc.identifier.scopus | 2-s2.0-85115160602 | en_US |
| dc.identifier.scopusquality | Q4 | en_US |
| dc.identifier.startpage | 29 | en_US |
| dc.identifier.uri | https://doi.org/10.1007/978-3-030-85672-4_3 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14619/3763 | |
| dc.identifier.volume | 1443 | en_US |
| dc.identifier.wos | WOS:001054800900003 | en_US |
| dc.identifier.wosquality | N/A | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Springer International Publishing Ag | en_US |
| dc.relation.ispartof | Optimization and Learning, Ola 2021 | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Adaptive operator selection | en_US |
| dc.subject | Reinforcement learning | en_US |
| dc.subject | Artificial bee colony | en_US |
| dc.subject | Uncapacitated Facility Location Problem (UFLP) | en_US |
| dc.title | Reinforcement Learning-Based Adaptive Operator Selection | en_US |
| dc.type | Conference Object | en_US |
