An improved butterfly optimization algorithm for training the feed-forward artificial neural networks

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dc.contributor.authorIrmak, Busra
dc.contributor.authorKarakoyun, Murat
dc.contributor.authorGulcu, Saban
dc.date.accessioned2024-02-23T13:43:53Z
dc.date.available2024-02-23T13:43:53Z
dc.date.issued2023
dc.departmentNEÜen_US
dc.description.abstractArtificial neural network (ANN) which is an information processing technique developed by modeling the nervous system of the human brain is one of the most powerful learning methods today. One of the factors that make ANN successful is its training algorithm. In this paper, an improved butterfly optimization algorithm (IBOA) based on the butterfly optimization algorithm was proposed for training the feed-forward artificial neural networks. The IBOA algorithm has the chaotic property which helps optimization algorithms to explore the search space more dynamically and globally. In the experiments, ten chaotic maps were used. The success of the IBOA algorithm was tested on 13 benchmark functions which are well known to those working on global optimization and are frequently used for testing and analysis of optimization algorithms. The Tent-mapped IBOA algorithm outperformed the other algorithms in most of the benchmark functions. Moreover, the success of the IBOA-MLP algorithm also has been tested on five classification datasets (xor, balloon, iris, breast cancer, and heart) and the IBOA-MLP algorithm was compared with four algorithms in the literature. According to the statistical performance metrics (sensitivity, specificity, precision, F1-score, and Friedman test), the IBOA-MLP outperformed the other algorithms and proved to be successful in training the feed-forward artificial neural networks.en_US
dc.identifier.doi10.1007/s00500-022-07592-w
dc.identifier.endpage3905en_US
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.issue7en_US
dc.identifier.pmid36284902en_US
dc.identifier.scopus2-s2.0-85140296544en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage3887en_US
dc.identifier.urihttps://doi.org/10.1007/s00500-022-07592-w
dc.identifier.urihttps://hdl.handle.net/20.500.12452/10965
dc.identifier.volume27en_US
dc.identifier.wosWOS:000870687000001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofSoft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectButterfly Optimization Algorithmen_US
dc.subjectChaosen_US
dc.subjectMultilayer Perceptronen_US
dc.subjectTraining Artificial Neural Networksen_US
dc.titleAn improved butterfly optimization algorithm for training the feed-forward artificial neural networksen_US
dc.typeArticleen_US

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