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

Fan Xue, Ching Yuen Chan, W. H. Ip, Chi Fai Cheung

Research output: Journal article publicationJournal articleAcademic researchpeer-review


Many 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.
Original languageEnglish
Pages (from-to)183-207
Number of pages25
JournalNETNOMICS: Economic Research and Electronic Networking
Issue number3
Publication statusPublished - 1 Oct 2011


  • Class association rules
  • Euclidean traveling salesman problem
  • Large-scale optimization
  • Metaheuristics
  • Supervised learning

ASJC Scopus subject areas

  • Information Systems
  • Economics and Econometrics
  • Computer Networks and Communications

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