A learning-based variable assignment weighting scheme for heuristic and exact searching in Euclidean traveling salesman problems
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. Copyright Springer Science+Business Media, LLC. 2011
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Bibliographic InfoArticle provided by Springer in its journal NETNOMICS: Economic Research and Electronic Networking.
Volume (Year): 12 (2011)
Issue (Month): 3 (October)
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Web page: http://www.springerlink.com/link.asp?id=102537
Supervised learning; Metaheuristics; Euclidean traveling salesman problem; Class association rules; Large-scale optimization;
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- Olafsson, Sigurdur & Li, Xiaonan, 2010. "Learning effective new single machine dispatching rules from optimal scheduling data," International Journal of Production Economics, Elsevier, vol. 128(1), pages 118-126, November.
- Helsgaun, Keld, 2000. "An effective implementation of the Lin-Kernighan traveling salesman heuristic," European Journal of Operational Research, Elsevier, vol. 126(1), pages 106-130, October.
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