Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/5569
DC FieldValueLanguage
dc.contributor.authorAbusnaina, Ahmed A.
dc.contributor.authorAbdullah, Rosni
dc.contributor.authorKattan, Ali
dc.date.accessioned2018-06-30T05:27:28Z
dc.date.available2018-06-30T05:27:28Z
dc.date.issued2018-02
dc.identifier.urihttp://hdl.handle.net/20.500.11889/5569
dc.description.abstractThe mussels wandering optimization (MWO) is a recent population-based metaheuristic optimization algorithm inspired ecologically by mussels’ movement behavior. The MWO has been used successfully for solving several optimization problems. This paper proposes an enhanced version of MWO, known as the enhanced-mussels wandering optimization (E-MWO) algorithm. The E-MWO aims to overcome the MWO shortcomings, such as lack in explorative ability and the possibility to fall in premature convergence. In addition, the E-MWO incorporates the self-adaptive feature for setting the value of a sensitive algorithm parameter. Then, it is adapted for supervised training of artificial neural networks, whereas pattern classification of real-world problems is considered. The obtained results indicate that the proposed method is a competitive alternative in terms of classification accuracy and achieve superior results in training time.en_US
dc.language.isoen_USen_US
dc.publisherDe Gruyteren_US
dc.subjectNeural networks (Computer science)en_US
dc.subjectSelf-adaptive softwareen_US
dc.subjectMathematical optimizationen_US
dc.subjectHeuristic algorithmsen_US
dc.subjectSignal processingen_US
dc.subjectArtificial intelligenceen_US
dc.titleSelf-adaptive mussels wandering optimization algorithm with application for artificial neural network trainingen_US
dc.typeArticleen_US
newfileds.departmentEngineering and Technologyen_US
newfileds.item-access-typebzuen_US
newfileds.thesis-prognoneen_US
newfileds.general-subjectComputers and Information Technology | الحاسوب وتكنولوجيا المعلوماتen_US
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