An efficient multivalued Hopfield network for the Traveling Salesman Problem

In this Letter we show that discrete multivalued Hopfield-type neural networks enable a relatively easy formulation of the Traveling Salesman Problem compared to the traditional Hopfield model. Thus, with the multivalued representation the network can be easily confined to feasible solutions, avoidi...

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Bibliographic Details
Published in:Neural processing letters 2001-12, Vol.14 (3), p.203-216
Main Authors: MERIDA-CASERMEIRO, E, GALAN-MARIN, G, MUNOZ-PEREZ, J
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Exact sciences and technology
Flows in networks. Combinatorial problems
Heuristic methods
Neural networks
Operational research and scientific management
Operational research. Management science
Traveling salesman problem
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Summary:In this Letter we show that discrete multivalued Hopfield-type neural networks enable a relatively easy formulation of the Traveling Salesman Problem compared to the traditional Hopfield model. Thus, with the multivalued representation the network can be easily confined to feasible solutions, avoiding the need to tune any parameter. An investigation into the performance of the network has led us to define updating rules based on simple effective heuristic algorithms, a technique that can not be usually incorporated into standard Hopfield models. Simulation results for Euclidean Traveling Salesman Problems taken from the data library TSPLIB [11] indicate that this multivalued neural approach is superior to the best neural network currently reported for this problem.
ISSN:1370-4621
1573-773X
DOI:10.1023/a:1012751230791