Improving Bi-Objective Shortest Path Search with Early Pruning.

dc.centroE.T.S.I. Informáticaes_ES
dc.contributor.authorMandow-Andaluz, Lorenzo
dc.contributor.authorPérez-de-la-Cruz-Molina, José Luis
dc.date.accessioned2023-12-01T07:42:01Z
dc.date.available2023-12-01T07:42:01Z
dc.date.created2023
dc.date.issued2023
dc.departamentoLenguajes y Ciencias de la Computación
dc.description.abstractBi-objective search problems are a useful generalization of shortest path search. This paper reviews some recent contributions for the solution of this problem with emphasis on the efficiency of the dominance checks required for pruning, and introduces a new algorithm that improves time efficiency over previous proposals. Experimental results are presented to show the performance improvement using a set of standard problems over bi-objective road maps.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Financiado por Plan Propio de Investigación de la Universidad de Málaga (UMA), Campus de Excelencia Internacional Andalucía Tech. Work supported by the Spanish Ministry of Science and Innovation, European Regional Development Fund (FEDER), Junta de Andalucía, and Universidad de Málaga through the research projects with reference IRIS PID2021-122812OB-I00, PID2021-122381OB-I00 and UMA20-FEDERJA-065.es_ES
dc.identifier.urihttps://hdl.handle.net/10630/28189
dc.language.isoenges_ES
dc.relation.eventdate30.09 - 04.10, 2023es_ES
dc.relation.eventplaceKrakow, Poloniaes_ES
dc.relation.eventtitle26th European Conference on Artificial Intelligence (ECAI 2023)es_ES
dc.rights.accessRightsopen accesses_ES
dc.subjectProgramación heurísticaes_ES
dc.subjectAlgoritmoses_ES
dc.subject.otherMultiobjective searches_ES
dc.subject.otherGraph searches_ES
dc.subject.otherShortest path problemes_ES
dc.subject.otherHeuristic searches_ES
dc.titleImproving Bi-Objective Shortest Path Search with Early Pruning.es_ES
dc.typeconference outputes_ES
dspace.entity.typePublication
relation.isAuthorOfPublicationb4b11711-73ab-4cd0-854c-8ab2735e829d
relation.isAuthorOfPublicationb7e65043-46cc-445b-8d8f-b4c7ad4f1c06
relation.isAuthorOfPublication.latestForDiscoveryb4b11711-73ab-4cd0-854c-8ab2735e829d

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
2023_ECAI-riuma.pdf
Size:
254.03 KB
Format:
Adobe Portable Document Format
Description: