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Artificial neural networks based optimization techniques: A review

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dc.citedby61
dc.contributor.authorAbdolrasol M.G.M.en_US
dc.contributor.authorSuhail Hussain S.M.en_US
dc.contributor.authorUstun T.S.en_US
dc.contributor.authorSarker M.R.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorMohamed R.en_US
dc.contributor.authorAli J.A.en_US
dc.contributor.authorMekhilef S.en_US
dc.contributor.authorMilad A.en_US
dc.contributor.authorid35796848700en_US
dc.contributor.authorid22035146400en_US
dc.contributor.authorid43761679200en_US
dc.contributor.authorid57537703000en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid7005169066en_US
dc.contributor.authorid56540826800en_US
dc.contributor.authorid57928298500en_US
dc.contributor.authorid57189499179en_US
dc.date.accessioned2023-05-29T09:05:35Z
dc.date.available2023-05-29T09:05:35Z
dc.date.issued2021
dc.description.abstractIn the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system. � 2021 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2689
dc.identifier.doi10.3390/electronics10212689
dc.identifier.issue21
dc.identifier.scopus2-s2.0-85118328878
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85118328878&doi=10.3390%2felectronics10212689&partnerID=40&md5=fa80379df58489d2a4ee06eb8a6d98dd
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25927
dc.identifier.volume10
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleElectronics (Switzerland)
dc.titleArtificial neural networks based optimization techniques: A reviewen_US
dc.typeReviewen_US
dspace.entity.typePublication
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