Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.neucom.2007.06.006
Title: An asynchronous recurrent linear threshold network approach to solving the traveling salesman problem
Authors: Teoh, E.J.
Tan, K.C. 
Tang, H.J.
Xiang, C. 
Goh, C.K.
Keywords: Genetic algorithms
Hopfield model
Linear threshold neurons
Recurrent neural networks
Traveling salesman problem
Issue Date: Mar-2008
Source: Teoh, E.J., Tan, K.C., Tang, H.J., Xiang, C., Goh, C.K. (2008-03). An asynchronous recurrent linear threshold network approach to solving the traveling salesman problem. Neurocomputing 71 (7-9) : 1359-1372. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neucom.2007.06.006
Abstract: In this paper, an approach to solving the classical Traveling Salesman Problem (TSP) using a recurrent network of linear threshold (LT) neurons is proposed. It maps the classical TSP onto a single-layered recurrent neural network by embedding the constraints of the problem directly into the dynamics of the network. The proposed method differs from the classical Hopfield network in the update of state dynamics as well as the use of network activation function. Furthermore, parameter settings for the proposed network are obtained using a genetic algorithm, which ensure a stable convergence of the network for different problems. Simulation results illustrate that the proposed network performs better than the classical Hopfield network for optimization. © 2007 Elsevier B.V. All rights reserved.
Source Title: Neurocomputing
URI: http://scholarbank.nus.edu.sg/handle/10635/50862
ISSN: 09252312
DOI: 10.1016/j.neucom.2007.06.006
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