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

Fan Xue, C. Chan (), W. Ip and C. Cheung

Netnomics, 2011, vol. 12, issue 3, 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. Copyright Springer Science+Business Media, LLC. 2011

Keywords: Supervised learning; Metaheuristics; Euclidean traveling salesman problem; Class association rules; Large-scale optimization (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1007/s11066-011-9064-7

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