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Solving the Terminal Assignment Problem Using a Local Search Genetic Algorithm

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dc.contributor.authorBernardino, Eugénia M.
dc.contributor.authorBernardino, Anabela M.
dc.contributor.authorSánchez-Pérez, Juan M.
dc.contributor.authorGómez-Pulido, Juan A.
dc.contributor.authorVega-Rodríguez, Miguel A.
dc.contributor.authorBernardino, Eugénia
dc.contributor.authorMoreira Bernardino, Anabela
dc.date.accessioned2025-04-21T14:23:33Z
dc.date.available2025-04-21T14:23:33Z
dc.date.issued2009
dc.descriptionInternational Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008)
dc.description.abstractTerminal assignment is an important issue in telecommunication networks optimization. The task here is to assign a given collection of terminals to a given collection of concentrators. The main objective is to minimize the link cost to form a network. This optimization task is an NP-complete problem. The intractability of this problem is a motivation for the pursuits of a local search genetic algorithm that produces approximate, rather than exact, solutions. In this paper, we explore one of the most successful emerging ideas combining local search with population-based search. Simulation results verify the effectiveness of the proposed method. The results show that our algorithm provides good solutions in a better running time.eng
dc.identifier.citationBernardino, E.M., Bernardino, A.M., Sánchez-Pérez, J.M., Gómez-Pulido, J.A., Vega-Rodríguez, M.A. (2009). Solving the Terminal Assignment Problem Using a Local Search Genetic Algorithm. In: Corchado, J.M., Rodríguez, S., Llinas, J., Molina, J.M. (eds) International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008). Advances in Soft Computing, vol 50. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85863-8_27.
dc.identifier.doi10.1007/978-3-540-85863-8_27
dc.identifier.isbn978-3-540-85862-1
dc.identifier.isbn978-3-540-85863-8
dc.identifier.issn1615-3871
dc.identifier.issn1860-0794
dc.identifier.urihttp://hdl.handle.net/10400.8/12820
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Berlin Heidelberg
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-540-85863-8_27
dc.relation.ispartofAdvances in Soft Computing
dc.relation.ispartofInternational Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008)
dc.rights.uriN/A
dc.subjectTerminal Assignment Problem
dc.subjectGenetic Algorithm
dc.subjectLocal Search Algorithm
dc.titleSolving the Terminal Assignment Problem Using a Local Search Genetic Algorithmeng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2008
oaire.citation.endPage234
oaire.citation.startPage225
oaire.citation.titleAdvances in Soft Computing
oaire.citation.volume50
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameBernardino
person.familyNameMoreira Bernardino
person.givenNameEugénia
person.givenNameAnabela
person.identifier.ciencia-id9616-F1BC-D8BD
person.identifier.ciencia-id081E-F3B8-316A
person.identifier.orcid0000-0001-5301-5853
person.identifier.orcid0000-0002-6561-5730
person.identifier.scopus-author-id24402754700
relation.isAuthorOfPublication893cf15c-eff8-4e43-949c-c1de6eb87599
relation.isAuthorOfPublication375ebe15-f84c-46a4-a3d9-6e4935a92187
relation.isAuthorOfPublication.latestForDiscovery893cf15c-eff8-4e43-949c-c1de6eb87599

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Terminal assignment is an important issue in telecommunication networks optimization. The task here is to assign a given collection of terminals to a given collection of concentrators. The main objective is to minimize the link cost to form a network. This optimization task is an NP-complete problem. The intractability of this problem is a motivation for the pursuits of a local search genetic algorithm that produces approximate, rather than exact, solutions. In this paper, we explore one of the most successful emerging ideas combining local search with population-based search. Simulation results verify the effectiveness of the proposed method. The results show that our algorithm provides good solutions in a better running time.
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