An asynchronous recurrent linear threshold network approach to solving the traveling salesman problem

Authors:
E. J. Teoh;K. C. Tan;H. J. Tang;C. Xiang;C. K. Goh
Affiliations:
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore;Queensland Brain Institute, Faculty of Biological and Chemical Sciences, University of Queensland, Australia;Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore
Venue:
Neurocomputing
Year:
2008

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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.