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Article: A learning-based variable assignment weighting scheme for heuristic and exact searching in Euclidean traveling salesman problems

TitleA learning-based variable assignment weighting scheme for heuristic and exact searching in Euclidean traveling salesman problems
Authors
KeywordsMetaheuristics
Supervised learning
Euclidean traveling salesman problem
Class association rules
Large-scale optimization
Issue Date2011
Citation
NETNOMICS: Economic Research and Electronic Networking, 2011, v. 12, n. 3, p. 183-207 How to Cite?
AbstractMany search algorithms have been successfully employed in combinatorial optimization in logistics practice. This paper presents an attempt to weight the variable assignments through supervised learning in subproblems. Heuristic and exact search methods can therefore test promising solutions first. The Euclidean Traveling Salesman Problem (ETSP) is employed as an example to demonstrate the presented method. Analysis shows that the rules can be approximately learned from the training samples from the subproblems and the near optimal tours. Experimental results on large-scale local search tests and small-scale branch-and-bound tests validate the effectiveness of the approach, especially when it is applied to industrial problems. © 2011 Springer Science+Business Media, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/230906
ISSN
2015 SCImago Journal Rankings: 0.286

 

DC FieldValueLanguage
dc.contributor.authorXue, Fan-
dc.contributor.authorChan, C. Y.-
dc.contributor.authorIp, W. H.-
dc.contributor.authorCheung, C. F.-
dc.date.accessioned2016-09-01T06:07:07Z-
dc.date.available2016-09-01T06:07:07Z-
dc.date.issued2011-
dc.identifier.citationNETNOMICS: Economic Research and Electronic Networking, 2011, v. 12, n. 3, p. 183-207-
dc.identifier.issn1385-9587-
dc.identifier.urihttp://hdl.handle.net/10722/230906-
dc.description.abstractMany search algorithms have been successfully employed in combinatorial optimization in logistics practice. This paper presents an attempt to weight the variable assignments through supervised learning in subproblems. Heuristic and exact search methods can therefore test promising solutions first. The Euclidean Traveling Salesman Problem (ETSP) is employed as an example to demonstrate the presented method. Analysis shows that the rules can be approximately learned from the training samples from the subproblems and the near optimal tours. Experimental results on large-scale local search tests and small-scale branch-and-bound tests validate the effectiveness of the approach, especially when it is applied to industrial problems. © 2011 Springer Science+Business Media, LLC.-
dc.languageeng-
dc.relation.ispartofNETNOMICS: Economic Research and Electronic Networking-
dc.subjectMetaheuristics-
dc.subjectSupervised learning-
dc.subjectEuclidean traveling salesman problem-
dc.subjectClass association rules-
dc.subjectLarge-scale optimization-
dc.titleA learning-based variable assignment weighting scheme for heuristic and exact searching in Euclidean traveling salesman problems-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s11066-011-9064-7-
dc.identifier.scopuseid_2-s2.0-84868458804-
dc.identifier.volume12-
dc.identifier.issue3-
dc.identifier.spage183-
dc.identifier.epage207-
dc.identifier.eissn1573-7071-

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