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A new Multiobjective Artificial Bee Colony algorithm to solve a real-world frequency assignment problem

2012-07-10Artigo científico Acesso restrito
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datacite.subject.fosCiências Naturais::Matemáticas
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg07:Energias Renováveis e Acessíveis
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
dc.contributor.authorMaximiano, Marisa
dc.contributor.authorVega-Rodríguez, Miguel A.
dc.contributor.authorGómez-Pulido, Juan A.
dc.contributor.authorSánchez-Pérez, Juan M.
dc.date.accessioned2026-03-12T15:07:57Z
dc.date.available2026-03-12T15:07:57Z
dc.date.issued2012-07-10
dc.description.abstractArtificial bee colony (ABC) is a recently introduced algorithm that models the behavior of honey bee swarm to address a multiobjective version for ABC, named Multiobjective Artificial Bee Colony algorithm (MO-ABC). We describe the methodology and results obtained when applying the new MO-ABC metaheuristic, which was developed to solve a real-world frequency assignment problem (FAP) in GSM networks. A precise mathematical formulation for this problem was used, where the frequency plans are evaluated using accurate interference information taken from a real GSM network. In this paper, our work is divided into two stages: In the first one, we have accurately tuned the algorithm parameters. Then, in the second step, we have compared the MO-ABC with previous versions of distinct multiobjective algorithms already developed to the same instances of the problem. As we will see, results show that this approach is able to obtain reasonable frequency plans when solving a real-world FAP. In the results analysis, we consider as complementary metrics the hypervolume indicator to measure the quality of the solutions to this problem as well as the coverage relation information.eng
dc.description.sponsorshipThis work was partially funded by the Spanish Ministry of Science and Innovation and FEDER under the contract TIN2008-06491-C04-04 (the MSTAR project). Thanks also to the Polytechnic Institute of Leiria, for the economic support offered to Marisa Maximiano to make this research.
dc.identifier.citationda Silva Maximiano, M., Vega-Rodríguez, M.A., Gómez-Pulido, J.A. et al. A new Multiobjective Artificial Bee Colony algorithm to solve a real-world frequency assignment problem. Neural Comput & Applic 22, 1447–1459 (2013). https://doi.org/10.1007/s00521-012-1046-7
dc.identifier.doi10.1007/s00521-012-1046-7
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.urihttp://hdl.handle.net/10400.8/15855
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relation.hasversionhttps://link.springer.com/article/10.1007/s00521-012-1046-7
dc.relation.ispartofNeural Computing and Applications
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMultiobjective optimization
dc.subjectFrequency assignment problem
dc.subjectMO-ABC
dc.subjectreal-world mobile network
dc.subjectMetaheuristics
dc.titleA new Multiobjective Artificial Bee Colony algorithm to solve a real-world frequency assignment problemeng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage1459
oaire.citation.issue7-8
oaire.citation.startPage1447
oaire.citation.titleNeural Computing and Applications
oaire.citation.volume22
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameMaximiano
person.givenNameMarisa
person.identifier.ciencia-idA919-B117-A16D
person.identifier.orcid0000-0002-1212-7864
person.identifier.scopus-author-id26767664900
relation.isAuthorOfPublication18092229-fa61-402b-978c-56b8127d46e9
relation.isAuthorOfPublication.latestForDiscovery18092229-fa61-402b-978c-56b8127d46e9

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