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dc.contributor.authorGaland, Lucie*
dc.contributor.authorIsmaili, Anisse*
dc.contributor.authorPerny, Patrice*
dc.contributor.authorSpanjaard, Olivier*
dc.date.accessioned2016-11-03T16:23:59Z
dc.date.available2016-11-03T16:23:59Z
dc.date.issued2013
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/15921
dc.language.isoenen
dc.subjectMultiojective Optimization
dc.subjectBidirectional Heuristic Search
dc.subjectMulti-ObjectiveShortest Path Problem
dc.subjectOWA Operator.
dc.subject.ddc003en
dc.titleBidirectional versus Unidirectional Heuristic Search for Multiojective Optimization in State Space Graphs
dc.typeCommunication / Conférence
dc.contributor.editoruniversityotherUniversité Pierre et Marie Curie
dc.description.abstractenMultiobjective heuristic search in state space graphs generally aims at determining theexact set of Pareto-optimal cost vectors associated to solution paths or an approximationof this Pareto set. When a preference model is available under the form of scalarizingfunction f, the search effort merely focuses on the direct determination of an f-optimaltradeoff within the Pareto-set. The solution algorithms proposed in this area rely on uni-directional approaches that develop a front of Pareto-optimal labels attached to subpathsfrom the initial node s to the goal nodes. When there is a single goal node t, explicitlyknown, and when search operators are reversible, a bidirectional search approach is possi-ble, interleaving and interacting searches, forward from s and backward from t. Althoughthe respective advantages of unidirectional approaches versus bidirectionnal approacheshave been widely discussed in the singleobjective case, they have not been investigated inthe setting of multiobjective search. The aim of this paper is to propose a first implemen-tation of multiobjective bidirectional heuristic search and to empirically assess his valuecompared to unidirectional search. Our comparison concerns both the determination ofthe entire Pareto set and the determination of OWA-optimal tradeoffs within the Paretoset. We provide results of numerical tests performed on random instances of path-planningproblems, both in terms of number of nodes expanded and in terms of computation times.
dc.subject.ddclabelRecherche opérationnelleen
dc.relation.conftitle22nd International Conference on Multiple Criteria Decision Making (MCDM 2013)
dc.relation.confcityMalaga
dc.relation.confcountrySPAIN
dc.relation.forthcomingnonen
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2017-03-21T16:05:36Z
hal.person.labIds989*
hal.person.labIds989*
hal.person.labIds*
hal.person.labIds*
hal.identifierhal-01391758*
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