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dc.creatorAlba, Enriquees
dc.creatorGarcía Nieto, José Manueles
dc.creatorTaheri, Javides
dc.creatorZomaya, Albertes
dc.date.accessioned2021-05-11T08:20:11Z
dc.date.available2021-05-11T08:20:11Z
dc.date.issued2008
dc.identifier.citationAlba, E., García Nieto, J.M., Taheri, J. y Zomaya, A. (2008). New Research in Nature Inspired Algorithms for Mobility Management in GSM Networks. En EvoWorkshops 2008: Workshops on Applications of Evolutionary Computation (1-10), Naples, Italy: Springer.
dc.identifier.isbn978-3-540-78760-0es
dc.identifier.issn0302-9743es
dc.identifier.urihttps://hdl.handle.net/11441/108838
dc.description.abstractMobile Location Management (MLM) is an important and complex telecommunication problem found in mobile cellular GSM networks. Basically, this problem consists in optimizing the number and location of paging cells to find the lowest location management cost. There is a need to develop techniques capable of operating with this complexity and used to solve a wide range of location management scenarios. Nature inspired algorithms are useful in this context since they have proved to be able to manage large combinatorial search spaces efficiently. The aim of this study is to assess the performance of two different nature inspired algorithms when tackling this problem. The first technique is a recent version of Particle Swarm Optimization based on geometric ideas. This approach is customized for the MLM problem by using the concept of Hamming spaces. The second algorithm consists of a combination of the Hopfield Neural Network coupled with a Ball Dropping technique. The location management cost of a network is embedded into the parameters of the Hopfield Neural Network. Both algorithms are evaluated and compared using a series of test instances based on realistic scenarios. The results are very encouraging for current applications, and show that the proposed techniques outperform existing methods in the literature.es
dc.formatapplication/pdfes
dc.format.extent10es
dc.language.isoenges
dc.publisherSpringeres
dc.relation.ispartofEvoWorkshops 2008: Workshops on Applications of Evolutionary Computation (2008), pp. 1-10.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMobile Location Managementes
dc.subjectGSM Cellular Networkses
dc.subjectGeometric Particle Swarm Optimizationes
dc.subjectHopfield Neural Networkes
dc.titleNew Research in Nature Inspired Algorithms for Mobility Management in GSM Networkses
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/submittedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificiales
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-540-78761-7_1es
dc.identifier.doi10.1007/978-3-540-78761-7_1es
dc.publication.initialPage1es
dc.publication.endPage10es
dc.eventtitleEvoWorkshops 2008: Workshops on Applications of Evolutionary Computationes
dc.eventinstitutionNaples, Italyes
dc.relation.publicationplaceBerlines

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