Introduction to algorithms
Polynomial time approximation schemes for Euclidean traveling salesman and other geometric problems
Journal of the ACM (JACM)
Using default ARTMAP for cancer classification with microRNA expression signatures
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Analysis of hyperspectral data with diffusion maps and fuzzy ART
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
ART properties of interest in engineering applications
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Application notes: memetic mission management
IEEE Computational Intelligence Magazine
BARTMAP: A viable structure for biclustering
Neural Networks
International Journal of Systems, Control and Communications
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The Traveling Salesman Problem (TSP) is a very hard optimization problem in the field of operations research. It has been shown to be NP-complete, and is an often-used benchmark for new optimization techniques. One of the main challenges with this problem is that standard, non-AI heuristic approaches such as the Lin-Kernighan algorithm (LK) and the chained LK variant are currently very effective and in wide use for the common fully connected, Euclidean variant that is considered here. This paper presents an algorithm that uses adaptive resonance theory (ART) in combination with a variation of the Lin-Kernighan local optimization algorithm to solve very large instances of the TSP. The primary advantage of this algorithm over traditional LK and chained-LK approaches is the increased scalability and parallelism allowed by the divide-and-conquer clustering paradigm. Tours obtained by the algorithm are lower quality, but scaling is much better and there is a high potential for increasing performance using parallel hardware.