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|Title:||An asynchronous recurrent linear threshold network approach to solving the traveling salesman problem||Authors:||Teoh, E.J.
Linear threshold neurons
Recurrent neural networks
Traveling salesman problem
|Issue Date:||Mar-2008||Citation:||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|
|Appears in Collections:||Staff Publications|
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