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dc.contributor.authorHernando, L.
dc.contributor.authorMendiburu, A.
dc.contributor.authorLozano, J.A. 
dc.date.accessioned2021-01-05T16:05:10Z
dc.date.available2021-01-05T16:05:10Z
dc.date.issued2020-06
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1230
dc.description.abstractA number of local search based algorithms have been designed to escape from the local optima, such as, iterated local search or variable neighborhood search. The neighborhood chosen for the local search as well as the escape technique play a key role in the performance of these algorithms. Of course, a specific strategy has a different effect on distinct problems or instances. In this paper, we focus on a permutation-based combinatorial optimization problem: the linear ordering problem. We provide a theoretical landscape analysis for the adjacent swap, the swap and the insert neighborhoods. By making connections to other different problems found in the Combinatorics field, we prove that there are some moves in the local optima that will necessarily return a worse or equal solution. The number of these non-better solutions that could be avoided by the escape techniques is considerably large with respect to the number of neighbors. This is a valuable information that can be included in any of those algorithms designed to escape from the local optima, increasing their efficiency.en_US
dc.description.sponsorshipTIN2016-78365-R IT1244-19en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectPermutation-based Combinatorial Optimization Problemsen_US
dc.subjectLinear Ordering Problemen_US
dc.subjectLocal Optimaen_US
dc.subjectLandscape Analysisen_US
dc.titleJourney to the center of the linear ordering problemen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.identifier.doi10.1145/3377930.3390241
dc.relation.publisherversionhttps://dl.acm.org/doi/10.1145/3377930.3390241en_US
dc.relation.projectIDES/1PE/SEV-2017-0718en_US
dc.relation.projectIDEUS/BERC/BERC.2018-2021en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titleGECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conferenceen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España