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Solving ring loading problems using bio-inspired algorithms

2011-03Artigo científico Acesso aberto
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dc.contributor.authorMoreira Bernardino, Anabela
dc.contributor.authorBernardino, Eugénia Moreira
dc.contributor.authorSanchez-Perez, Juan Manuel
dc.contributor.authorGomez-Pulido, Juan Antonio
dc.contributor.authorVega-Rodriguez, Miguel Angel
dc.date.accessioned2026-02-06T14:51:07Z
dc.date.available2026-02-06T14:51:07Z
dc.date.issued2011-03
dc.description.abstractIn the last years, several combinatorial optimisation problems have arisen in the communication networks field. In many cases, to solve these problems it is necessary the use of emergent optimisation algorithms. The Weighted Ring Loading Problem (WRLP) is an important optimisation problem in the communication optical network field. When managed properly, the ring networks are uniquely suited to deliver a large amount of bandwidth in a reliable and inexpensive way. An optimal load balancing is very important, as it increases the system's capacity and improves the overall ring performance. The WRLP consists on the design, in a communication network of a transmission route (direct path) for each request, such that high load on the arcs/edges is avoided, where an arc is an edge endowed with a direction. In this paper we study this problem in two different ring types: Synchronous Optical NETworking (SONET) rings and Resilient Packet Ring (RPR). In RPR the purpose is to minimise the maximum load on the ring Arcs (WRALP). In SONET rings the purpose is to minimise the maximum load on the ring Edges (WRELP). The load of an arc is defined as the total weight of those requests that are routed through the arc in its direction and the load of an edge is the total weight of the routes traversing the edge in either direction. In this paper we study both problems without demand splitting and we propose three bio-inspired algorithms: Genetic Algorithm with multiple operators, Hybrid Differential Evolution with a multiple strategy and Hybrid Discrete Particle Swarm Optimisation. We also perform comparisons with other algorithms from literature. Simulation results verify the effectiveness of the proposed algorithms.eng
dc.description.sponsorshipThis work has been partially supported by the Polytechnic Institute of Leiria (Portugal) and the MSTAR Project Reference: TIN2008-06491-C04-04/TIN (MICINN Spain).
dc.identifier.citationAnabela Moreira Bernardino, Eugénia Moreira Bernardino, Juan Manuel Sanchez-Perez, Juan Antonio Gomez-Pulido, Miguel Angel Vega-Rodriguez, Solving ring loading problems using bio-inspired algorithms, Journal of Network and Computer Applications, Volume 34, Issue 2, 2011, Pages 668-685, ISSN 1084-8045, https://doi.org/10.1016/j.jnca.2010.11.003.
dc.identifier.doi10.1016/j.jnca.2010.11.003
dc.identifier.issn1084-8045
dc.identifier.urihttp://hdl.handle.net/10400.8/15560
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier BV
dc.relation.hasversionhttps://www.sciencedirect.com/science/article/pii/S1084804510002018
dc.relation.ispartofJournal of Network and Computer Applications
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCommunication networks
dc.subjectBio-inspired algorithms
dc.subjectOptimisation algorithms
dc.subjectSwarm Intelligence
dc.subjectWeighted Ring Arc-Loading Problem
dc.subjectWeighted Ring Edge-Loading Problem
dc.titleSolving ring loading problems using bio-inspired algorithmseng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage685
oaire.citation.issue2
oaire.citation.startPage668
oaire.citation.titleJournal of Network and Computer Applications
oaire.citation.volume34
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameMoreira Bernardino
person.familyNameBernardino
person.givenNameAnabela
person.givenNameEugénia
person.identifier.ciencia-id081E-F3B8-316A
person.identifier.ciencia-id9616-F1BC-D8BD
person.identifier.orcid0000-0002-6561-5730
person.identifier.orcid0000-0001-5301-5853
person.identifier.scopus-author-id24402754700
relation.isAuthorOfPublication375ebe15-f84c-46a4-a3d9-6e4935a92187
relation.isAuthorOfPublication893cf15c-eff8-4e43-949c-c1de6eb87599
relation.isAuthorOfPublication.latestForDiscovery375ebe15-f84c-46a4-a3d9-6e4935a92187

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