dc.creator | Alba, Enrique | es |
dc.creator | García Nieto, José Manuel | es |
dc.creator | Taheri, Javid | es |
dc.creator | Zomaya, Albert | es |
dc.date.accessioned | 2021-05-11T08:20:11Z | |
dc.date.available | 2021-05-11T08:20:11Z | |
dc.date.issued | 2008 | |
dc.identifier.citation | Alba, 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.isbn | 978-3-540-78760-0 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | https://hdl.handle.net/11441/108838 | |
dc.description.abstract | Mobile 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.format | application/pdf | es |
dc.format.extent | 10 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | EvoWorkshops 2008: Workshops on Applications of Evolutionary Computation (2008), pp. 1-10. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Mobile Location Management | es |
dc.subject | GSM Cellular Networks | es |
dc.subject | Geometric Particle Swarm Optimization | es |
dc.subject | Hopfield Neural Network | es |
dc.title | New Research in Nature Inspired Algorithms for Mobility Management in GSM Networks | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-540-78761-7_1 | es |
dc.identifier.doi | 10.1007/978-3-540-78761-7_1 | es |
dc.publication.initialPage | 1 | es |
dc.publication.endPage | 10 | es |
dc.eventtitle | EvoWorkshops 2008: Workshops on Applications of Evolutionary Computation | es |
dc.eventinstitution | Naples, Italy | es |
dc.relation.publicationplace | Berlin | es |