Publication
Evolutionary Swarm based algorithms to minimise the link cost in Communication Networks
| datacite.subject.sdg | 08:Trabalho Digno e Crescimento Económico | |
| datacite.subject.sdg | 09:Indústria, Inovação e Infraestruturas | |
| datacite.subject.sdg | 10:Reduzir as Desigualdades | |
| dc.contributor.author | Moreira Bernardino, Anabela | |
| dc.contributor.author | Sánchez-Pérez, Juan Manuel | |
| dc.contributor.author | Gómez-Pulido, Juan Antonio | |
| dc.contributor.author | Vega-Rodríguez, Miguel Ángel | |
| dc.contributor.author | Bernardino, Eugénia Moreira | |
| dc.date.accessioned | 2025-10-29T10:10:26Z | |
| dc.date.available | 2025-10-29T10:10:26Z | |
| dc.date.issued | 2012 | |
| dc.description.abstract | In the last decades, nature-inspired algorithms have been widely used to solve complex combinatorial optimisation problems. Among them, Evolutionary Algorithms (EAs) and Swarm Intelligence (SI) algorithms have been extensively employed as search and optimisation tools in various problem domains. Evolutionary and Swarm Intelligent algorithms are Artificial Intelligence (AI) techniques, inspired by natural evolution and adaptation. This paper presents two new nature-inspired algorithms, which use concepts of EAs and SI. The combination of EAs and SI algorithms can unify the fast speed of EAs to find global solutions and the good precision of SI algorithms to find good solutions using the feedback information. The proposed algorithms are applied to a complex NP-hard optimisation problem - the Terminal Assignment Problem (TAP). The objective is to minimise the link cost to form a network. The proposed algorithms are compared with several EAs and SI algorithms from literature. We show that the proposed algorithms are suitable for solving very large scaled problems in short computational times. | eng |
| dc.description.sponsorship | This work has been partially supported by the Polytechnic Institute of Leiria (Portugal) and the MSTAR Project. Reference: TIN 2008-06491-C04-04/TIN (MICINN Spain). | |
| dc.identifier.citation | Bernardino, E.M., Bernardino, A.M., Sánchez-Pérez, J.M. et al. Evolutionary Swarm based algorithms to minimise the link cost in Communication Networks. Int J Comput Intell Syst 5, 745–761 (2012). https://doi.org/10.1080/18756891.2012.718157 | |
| dc.identifier.doi | 10.1080/18756891.2012.718157 | |
| dc.identifier.issn | 1875-6883 | |
| dc.identifier.uri | http://hdl.handle.net/10400.8/14420 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Springer Science and Business Media LLC | |
| dc.relation.hasversion | https://link.springer.com/article/10.1080/18756891.2012.718157 | |
| dc.relation.ispartof | International Journal of Computational Intelligence Systems | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Evolutionary Algorithms | |
| dc.subject | Swarm Intelligence | |
| dc.subject | Terminal Assignment Problem | |
| dc.subject | Genetic algorithm with a new swarm mutation operator | |
| dc.subject | Queen-bee Evolutionary Algorithm | |
| dc.title | Evolutionary Swarm based algorithms to minimise the link cost in Communication Networks | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 761 | |
| oaire.citation.issue | 4 | |
| oaire.citation.startPage | 745 | |
| oaire.citation.title | International Journal of Computational Intelligence Systems | |
| oaire.citation.volume | 5 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Moreira Bernardino | |
| person.familyName | Bernardino | |
| person.givenName | Anabela | |
| person.givenName | Eugénia | |
| person.identifier.ciencia-id | 081E-F3B8-316A | |
| person.identifier.ciencia-id | 9616-F1BC-D8BD | |
| person.identifier.orcid | 0000-0002-6561-5730 | |
| person.identifier.orcid | 0000-0001-5301-5853 | |
| person.identifier.scopus-author-id | 24402754700 | |
| relation.isAuthorOfPublication | 375ebe15-f84c-46a4-a3d9-6e4935a92187 | |
| relation.isAuthorOfPublication | 893cf15c-eff8-4e43-949c-c1de6eb87599 | |
| relation.isAuthorOfPublication.latestForDiscovery | 375ebe15-f84c-46a4-a3d9-6e4935a92187 |