A neural network method for minimum delay routing in packet-switched networks

In this paper, a neural networks (NNs) based two-phase routing algorithm is proposed. The aim of the first phase is to find a set of alternative route s for each commodity, while the traffic of each commodity is optimally dist ributed on the alternative routes in the second phase. Our final goal is to route all messages so that the average time delay of a message is minimize d. Since the Hopfield neural network (HNN) can only solve problems whose en ergy functions can be expressed as quadratic forms, the expression of the a verage time delay of a packet needs first to be simplified and then explici tly included into the energy function. Our algorithm is applied to two netw ork models, both of which have been previously analyzed by other researcher s using mathematical methods. Compared with previous results, the proposed algorithm considerably reduces the time delay a packet encounters. A large number of experiments also indicate that the proposed algorithm has very go od stability. Our work provides a possible routing policy for future high-s peed communication networks due to the fact that a hardware-implemented NN can achieve an extremely high response speed. (C) 2001 Elsevier Science B.V . All rights reserved.