NEURAL NETWORKS FOR SHORTEST-PATH COMPUTATION AND ROUTING IN COMPUTER-NETWORKS

Recently neural networks have been proposed as new computational tools for solving constrained optimization problems. This paper is concerned with the application of neural networks to the optimum routing problem in packet-switched computer networks, where the goal is to minimize the network-wide average time delay. Under appropriate assumptions; the optimum routing algorithm relies heavily on shortest path computations that have to be carried gut in real time. For this purpose an;efficient neural network shortest path algorithm, that is an improved version of previously suggested Hopfield models, is proposed. The general principles involved in the design of the proposed neural network are discussed in detail. The computational power of the proposed neural model is demonstrated through computer simulations. One of the main features of the proposed model is that it will enable the routing algorithm to be implemented in real time and also to be adaptive to changes in link costs and network topology.