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Master-leader-slave cuckoo search with parameter control for ANN optimization and its real-world application to water quality prediction

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dc.citedby15
dc.contributor.authorJaddi N.S.en_US
dc.contributor.authorAbdullah S.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorid36716354300en_US
dc.contributor.authorid23395783300en_US
dc.contributor.authorid55636320055en_US
dc.date.accessioned2023-05-29T06:40:52Z
dc.date.available2023-05-29T06:40:52Z
dc.date.issued2017
dc.descriptionartificial neural network; human; leadership; prediction; slave; species; time series analysis; water quality; algorithm; artificial neural network; computer simulation; standards; theoretical model; water quality; Algorithms; Computer Simulation; Models, Theoretical; Neural Networks (Computer); Water Qualityen_US
dc.description.abstractArtificial neural networks (ANNs) have been employed to solve a broad variety of tasks. The selection of an ANN model with appropriate weights is important in achieving accurate results. This paper presents an optimization strategy for ANN model selection based on the cuckoo search (CS) algorithm, which is rooted in the obligate brood parasitic actions of some cuckoo species. In order to enhance the convergence ability of basic CS, some modifications are proposed. The fraction Pa of the n nests replaced by new nests is a fixed parameter in basic CS. As the selection of Pa is a challenging issue and has a direct effect on exploration and therefore on convergence ability, in this work the Pa is set to a maximum value at initialization to achieve more exploration in early iterations and it is decreased during the search to achieve more exploitation in later iterations until it reaches the minimum value in the final iteration. In addition, a novel master-leader-slave multi-population strategy is used where the slaves employ the best fitness function among all slaves, which is selected by the leader under a certain condition. This fitness function is used for subsequent L�vy flights. In each iteration a copy of the best solution of each slave is migrated to the master and then the best solution is found by the master. The method is tested on benchmark classification and time series prediction problems and the statistical analysis proves the ability of the method. This method is also applied to a real-world water quality prediction problem with promising results. � 2017 Jaddi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNoe0170372
dc.identifier.doi10.1371/journal.pone.0170372
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85011661902
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85011661902&doi=10.1371%2fjournal.pone.0170372&partnerID=40&md5=afd29c3f4a23559d03bd8a53c4e2aa66
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23479
dc.identifier.volume12
dc.publisherPublic Library of Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitlePLoS ONE
dc.titleMaster-leader-slave cuckoo search with parameter control for ANN optimization and its real-world application to water quality predictionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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