dc.contributor.author | Badem, Hasan | |
dc.contributor.author | Baştürk, Alper | |
dc.contributor.author | Çalışkan, Abdullah | |
dc.contributor.author | Yüksel, Mehmet Emin | |
dc.date.accessioned | 12.07.201910:50:10 | |
dc.date.accessioned | 2019-07-12T22:06:05Z | |
dc.date.available | 12.07.201910:50:10 | |
dc.date.available | 2019-07-12T22:06:05Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Badem, H., Basturk, A., Caliskan, A., Yuksel, M.E. (2018).
A new hybrid optimization method combining artificial bee colony and limited-memory BFGS algorithms for efficient numerical optimization. Applied Soft Computing Journal, 70, pp. 826-844.
https://doi.org/10.1016/j.asoc.2018.06.010 | |
dc.identifier.issn | 1568-4946 | |
dc.identifier.issn | 1872-9681 | |
dc.identifier.uri | https://doi.org/10.1016/j.asoc.2018.06.010 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12508/635 | |
dc.description | WOS: 000443296000054 | en_US |
dc.description.abstract | In this paper, a new optimization method, which is developed especially for optimization of functions with a large number of local minima, is presented. The proposed method is a hybrid optimization algorithm which employs the artificial bee colony (ABC) and limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithms for combining their powerful features. The most prominent feature of the proposed method over other methods is that it provides accurate results and valuable convergence speeds, as well as easy implementation at the same time. Extensive simulation results supported by detailed statistical analyses show that the proposed method can be used for efficient optimization of functions including well-known benchmark functions and CEC2016 competition functions. (C) 2018 Elsevier B.V. All rights reserved. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier Science | en_US |
dc.relation.isversionof | 10.1016/j.asoc.2018.06.010 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Artificial bee colony algorithm | en_US |
dc.subject | L-BEGS | en_US |
dc.subject | Global optimization | en_US |
dc.subject | Swarm intelligence | en_US |
dc.subject.classification | Computer Science | en_US |
dc.subject.classification | Artificial Intelligence | en_US |
dc.subject.classification | Computer Science | en_US |
dc.subject.classification | Interdisciplinary Applications | en_US |
dc.subject.classification | Bee | Exploration And Exploitation | Colony | en_US |
dc.subject.other | Search | en_US |
dc.subject.other | Performance | en_US |
dc.subject.other | Evolutionary algorithms | en_US |
dc.subject.other | Global optimization | en_US |
dc.subject.other | Swarm intelligence | en_US |
dc.subject.other | Artificial bee colonies | en_US |
dc.subject.other | Artificial bee colonies (ABC) | en_US |
dc.subject.other | Hybrid optimization algorithm | en_US |
dc.subject.other | Hybrid optimization method | en_US |
dc.subject.other | Limited memory Broyden-Fletcher-Goldfarb-Shanno | en_US |
dc.subject.other | Numerical optimizations | en_US |
dc.subject.other | Numerical methods | en_US |
dc.title | A new hybrid optimization method combining artificial bee colony and limited-memory BFGS algorithms for efficient numerical optimization | en_US |
dc.type | article | en_US |
dc.relation.journal | Applied Soft Computing Journal | en_US |
dc.contributor.department | Mühendislik ve Doğa Bilimleri Fakültesi -- Biyomedikal Mühendisliği Bölümü | en_US |
dc.identifier.volume | 70 | en_US |
dc.identifier.startpage | 826 | en_US |
dc.identifier.endpage | 844 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.contributor.isteauthor | Çalışkan, Abdullah | en_US |
dc.relation.index | Web of Science - Scopus | en_US |
dc.relation.index | Web of Science Core Collection - Science Citation Index Expanded | en_US |