An Improved Animal Migration Optimization Algorithm to Train the Feed-Forward Artificial Neural Networks

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dc.contributor.authorGulcu, Saban
dc.date.accessioned2024-02-23T14:00:04Z
dc.date.available2024-02-23T14:00:04Z
dc.date.issued2022
dc.departmentNEÜen_US
dc.description.abstractThe most important and demanding part of the artificial neural network is the training process which involves finding the most suitable values for the weights in the network architecture, a challenging optimization problem. Gradient approaches and the meta-heuristic approaches are two methods extensively used to optimize the weights in the network. Gradient approaches have serious disadvantages including getting stuck in local optima, inadequate exploration, etc. To overcome these disadvantages, meta-heuristic approaches are preferred in training the artificial neural network instead of gradient methods. Therefore, in this study, an improved animal migration optimization algorithm with the Levy flight feature was proposed to train the multilayer perceptron. The proposed hybrid algorithm is named IAMO-MLP. The main contributions of this article are that the IAMO algorithm was developed, the IAMO-MLP algorithm can successfully escape from local optima, and the initial positions did not affect the performance of the IAMO-MLP algorithm. The enhanced algorithm was tested and validated against a wider set of benchmark functions and indicated that it substantially outperformed the original implementation. Afterward, the IAMO-MLP was compared with ten algorithms on five classification problems (xor, balloon, iris, breast cancer, and heart) and one real-world problem in terms of mean squared error, classification accuracy, and nonparametric statistical Friedman test. According to the results, the IAMO was successful in training the multilayer perceptron.en_US
dc.identifier.doi10.1007/s13369-021-06286-z
dc.identifier.endpage9581en_US
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.issue8en_US
dc.identifier.pmid34777937en_US
dc.identifier.scopus2-s2.0-85119158077en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage9557en_US
dc.identifier.urihttps://doi.org/10.1007/s13369-021-06286-z
dc.identifier.urihttps://hdl.handle.net/20.500.12452/11446
dc.identifier.volume47en_US
dc.identifier.wosWOS:000716870300003en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofArabian Journal For Science And Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAnimal Migration Optimization Algorithmen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectCivil Engineeringen_US
dc.subjectLevy Flighten_US
dc.subjectMultilayer Perceptronen_US
dc.subjectTraining Of Artificial Neural Networksen_US
dc.titleAn Improved Animal Migration Optimization Algorithm to Train the Feed-Forward Artificial Neural Networksen_US
dc.typeArticleen_US

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