Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/6721
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Title: A learning-based variable assignment weighting scheme for heuristic and exact searching in Euclidean traveling salesman problems
Authors: Xue, F
Chan, CY 
Ip, WH 
Cheung, CF 
Issue Date: Oct-2011
Source: NETNOMICS: Economic research and electronic networking, Oct. 2011, v. 12, no. 3, p. 183-207
Abstract: 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.
Keywords: Supervised learning
Metaheuristics
Euclidean traveling salesman problem
Class association rules
Large-scale optimization
Publisher: Springer
Journal: NETNOMICS: Economic research and electronic networking 
ISSN: 1385-9587 (print)
1573-7071 (online)
DOI: 10.1007/s11066-011-9064-7
Rights: © Springer Science+Business Media, LLC 2011
The published article is located at http://dx.doi.org/10.1007/s11066-011-9064-7. The final publication is available at link.springer.com.
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