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dc.contributor.authorMarti, Rafael
dc.contributor.authorLozano Alonso, José Antonio
dc.contributor.authorMendiburu Alberro, Alexander
dc.contributor.authorHernando Rodríguez, Leticia ORCID
dc.date.accessioned2024-02-11T11:00:49Z
dc.date.available2024-02-11T11:00:49Z
dc.date.issued2018-08-27
dc.identifier.citationHandbook of Heuristics: 155-175 (2018)es_ES
dc.identifier.isbn978-3-319-07123-7
dc.identifier.urihttp://hdl.handle.net/10810/66013
dc.description.abstract[EN]Multi-start procedures were originally conceived as a way to exploit a local or neighborhood search procedure, by simply applying it from multiple random initial solutions. Modern multi-start methods usually incorporate a powerful form of diversification in the generation of solutions to help overcome local optimality. Different metaheuristics, such as GRASP or tabu search, have been applied to this end. This survey briefly sketches historical developments that have motivated the field, and then focuses on modern contributions that define the current state-of-the-art. We consider the two classic categories of multi-start methods according to their domain of application: global optimization and combinatorial optimization. Additionally, we review several methods to estimate the number of local optima in combinatorial problems. The estimation of this number can help to establish the complexity of a given instance, and also to choose the most convenient neighborhood, which is especially interesting in the context of multi-start methods.es_ES
dc.description.sponsorshipThe first author was partially supported by grants TIN2009-07516 and TIN2012-35632 of Ministerio de Ciencia e Innovaci´on of Spain. The third author was partially sup- ported by grants 308687/2010-8 and 483243/2010-8 of CNPq, Conselho Nacional de Desenvolvimento Cient´ıfico e Tecnol´ogico of Brazil and by grants E-26/110.552/2010 and E-26/102.954/2011 of FAPERJ, Funda¸c˜ao de Amparo `a Pesquisa do Estado do Rio de Janeiro, Brazil. This work has been partially supported by the Saiotek and Research Groups 2013-2018 (IT- 609-13) programs (Basque Government), TIN2013- 41272P (Spanish Ministry of Science and Innovation), COMBIOMED network in com- putational biomedicine (Carlos III Health Institute).
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relationinfo:eu-repo/grantAgreement/MICIN/IN2013-41272P
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectmetaheuristicses_ES
dc.subjectmulti-start methodses_ES
dc.subjectlocal optima estimationes_ES
dc.titleMulti-Start Methodses_ES
dc.typeinfo:eu-repo/semantics/bookPartes_ES
dc.rights.holder© 2018, Springer International Publishing AG, part of Springer Nature
dc.relation.publisherversionhttps://link.springer.com/referenceworkentry/10.1007/978-3-319-07124-4_1
dc.identifier.doi/10.1007/978-3-319-07124-4_1
dc.departamentoesCiencia de la Computación e Inteligencia Artificial
dc.departamentoeuKonputazio Zientzia eta Adimen Artifiziala


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