An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training

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dc.contributor.author Karaboğa, Derviş
dc.contributor.author Kaya, Ebubekir
dc.date.accessioned 2022-12-14T07:21:23Z
dc.date.available 2022-12-14T07:21:23Z
dc.date.issued 2016-12
dc.identifier.uri https://www.sciencedirect.com/science/article/abs/pii/S156849461630374X?via%3Dihub
dc.identifier.uri http://hdl.handle.net/20.500.11787/7842
dc.description.abstract In this study, we propose an Adaptive and Hybrid Artificial Bee Colony (aABC) algorithm to train ANFIS. Unlike the standard ABC algorithm, two new parameters are utilized in the solution search equation. These are arithmetic crossover rate and adaptivity coefficient. aABC algorithm gains the rapid convergence feature with the usage of arithmetic crossover and it is applied on two different problem groups and its performance is measured. Firstly, it is performed over 10 numerical ‘benchmark functions’. The results show that aABC algorithm is more efficient than standard ABC algorithm. Secondly, ANFIS is trained by using aABC algorithm to identify the nonlinear dynamic systems. Each application begins with the randomly selected initial population and then average RMSE is obtained. For four examples considered in ANFIS training, train error values are respectively computed as 0.0344, 0.0232, 0.0152 and 0.0205. Also, test error values for these examples are respectively found as 0.0255, 0.0202, 0.0146 and 0.0295. Although it varies according to the examples, performance increase between 4.51% and 33.33% occurs. Additionally, it is seen that aABC algorithm converges bettter than ABC algorithm in the all examples. The obtained results are compared with the neuro-fuzzy based approaches which are commonly used in the literature and it is seen that the proposed ABC variant can be efficiently used for ANFIS training. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1016/j.asoc.2016.07.039 tr_TR
dc.rights info:eu-repo/semantics/restrictedAccess tr_TR
dc.subject ANFIS tr_TR
dc.subject Neuro-fuzzy tr_TR
dc.subject Artificial bee colony algorithm tr_TR
dc.subject Swarm intelligence tr_TR
dc.subject Nonlinear system identification tr_TR
dc.title An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training tr_TR
dc.type article tr_TR
dc.relation.journal Applied Soft Computing tr_TR
dc.contributor.department Nevşehir Hacı Bektaş Veli Üniversitesi/mühendislik-mimarlık fakültesi/bilgisayar mühendisliği bölümü/bilgisayar yazılımı anabilim dalı tr_TR
dc.contributor.authorID 133069 tr_TR
dc.contributor.authorID 108481 tr_TR
dc.identifier.volume 49 tr_TR
dc.identifier.startpage 423 tr_TR
dc.identifier.endpage 436 tr_TR


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