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dc.contributor.authorLópez Ibáñez, Manuel
dc.contributor.authorBlum, Christian
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.date.accessioned2016-05-10T12:40:05Z
dc.date.available2016-05-10T12:40:05Z
dc.date.issued2008-10
dc.identifier.citationLópez, M., Blum, C. "Beam-ACO based on stochastic sampling: a case study on the TSP with time windows". 2008.
dc.identifier.urihttp://hdl.handle.net/2117/86872
dc.description.abstractBeam-ACO algorithms are hybrid methods that combine the metaheuristic ant colony optimization with beam search. They heavily rely on accurate and computationally inexpensive bounding information for choosing between different partial solutions during the solution construction process. In this work we present the use of stochastic sampling as a useful alternative to bounding information in cases were computing accurate bounding information is too expensive. As a case study we choose the well-known travelling salesman problem with time windows. Our results clearly demonstrate that Beam-ACO, even when bounding information is replaced by stochastic sampling, may have important advantages over standard ACO algorithms.
dc.format.extent15 p.
dc.language.isoeng
dc.relation.ispartofseriesLSI-08-28-R
dc.subjectÀrees temàtiques de la UPC::Informàtica::Informàtica teòrica
dc.subject.otherBeam-ACO
dc.subject.otherStochastic sampling
dc.subject.otherTravelling salesman problem with time windows
dc.titleBeam-ACO based on stochastic sampling: a case study on the TSP with time windows
dc.typeExternal research report
dc.rights.accessOpen Access
drac.iddocument1836448
dc.description.versionPostprint (published version)
upcommons.citation.authorLópez, M.; Blum, C.
upcommons.citation.publishedtrue


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