Show simple item record

dc.contributor.authorRuiz Ruiz, Hector Efrain
dc.contributor.authorAlbareda Sambola, Maria
dc.contributor.authorFernández Aréizaga, Elena
dc.contributor.authorResende, Mauricio G. C.
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.date.accessioned2015-04-29T16:50:22Z
dc.date.available2018-06-01T00:30:24Z
dc.date.created2015-05
dc.date.issued2015-05
dc.identifier.citationRuiz, H. [et al.]. A biased random-key genetic algorithm for the capacitated minimum spanning tree problem. "Computers & operations research", Maig 2015, vol. 57, p. 95-108.
dc.identifier.issn0305-0548
dc.identifier.urihttp://hdl.handle.net/2117/27676
dc.description.abstractThis paper focuses on the capacitated minimum spanning tree(CMST)problem.Given a central processor and a set of remote terminals with specified demands for traffic that must flow between the central processor and terminals,the goal is to design a minimum cost network to carry this demand. Potential links exist between any pair of terminals and between the central processor and the terminals. Each potential link can be included in the design at a given cost.The CMST problem is to design a minimum-cost network connecting the terminals with the central processor so that the flow on any arc of the network is at most Q. A biased random-keygenetic algorithm(BRKGA)is a metaheuristic for combinatorial optimization which evolves a population of random vectors that encode solutions to the combinatorial optimization problem.This paper explores several solution encodings as well as different strategies for some steps of the algorithm and finally proposes a BRKGA heuristic for the CMST problem. Computational experiments are presented showing the effectivenes sof the approach:Seven newbest- known solutions are presented for the set of benchmark instances used in the experiments.
dc.format.extent14 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa
dc.subject.lcshCombinatorial optimization
dc.subject.lcshHeuristic
dc.subject.lcshComputer algorithms
dc.subject.otherOptimization
dc.subject.otherCombinatorial optimization
dc.subject.otherNetworks
dc.subject.otherGraphs
dc.subject.otherTrees
dc.subject.otherSpanning trees
dc.subject.otherCapacitated minimumspanningtree
dc.subject.otherHeuristics
dc.subject.otherBiased random-keygeneticalgorithm
dc.titleA biased random-key genetic algorithm for the capacitated minimum spanning tree problem
dc.typeArticle
dc.subject.lemacOptimització combinatòria
dc.subject.lemacHeurística
dc.subject.lemacAlgorismes genètics
dc.contributor.groupUniversitat Politècnica de Catalunya. GNOM - Grup d'Optimització Numèrica i Modelització
dc.identifier.doi10.1016/j.cor.2014.11.011
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0305054814003153
dc.rights.accessOpen Access
drac.iddocument15571786
dc.description.versionPostprint (author’s final draft)
upcommons.citation.authorRuiz, H.; Albareda-Sambola, M.; Fernandez, E.; Resende, M.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameComputers & operations research
upcommons.citation.volume57
upcommons.citation.startingPage95
upcommons.citation.endingPage108


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder