In addition to the classical heuristic algorithms of operations research, there have also been several approaches based on artificial neural networks for solving the traveling salesman problem. Their efficiency, however, decreases as the problem size (number of cities) increases. A technique to reduce the complexity of a large-scale traveling salesman problem (TSP) instance is to decompose or partition it into smaller subproblems. In this paper, we introduce an all-neural decomposition heuristic that is based on a recent self-organizing map called KNIES, which has been successfully implemented for solving both the Euclidean traveling salesman problem and the Euclidean Hamiltonian path problem. Our solution for the Euclidean TSP proceeds by solving the Euclidean HPP for the subproblems, and then patching these solutions together. No such all-neural solution has ever been reported.

Additional Metadata
Keywords Combinatorial optimization, Decomposition, Euclidean traveling salesman problem (TSP), Kohonen, Neural networks, Self-organizing maps
Persistent URL dx.doi.org/10.1109/TNN.2003.811562
Journal IEEE Transactions on Neural Networks
Citation
Aras, N. (Necati), Altmel, I.K. (I. Kuban), & Oommen, J. (2003). A kohonen-like decomposition method for the Euclidean traveling salesman problem - KNIES_DECOMPOSE. IEEE Transactions on Neural Networks, 14(4), 869–890. doi:10.1109/TNN.2003.811562