Neural Network for Optimization of Routing in Communication Networks

The efficient neural network algorithm for optimization of routing in communication networks is suggested. As it was known from literature, different optimization and ill-defined problems may be resolved using appropriately designed neural networks, due to their high computational speed and the possibility of working with uncertain data. Under some assumptions, the routing in packet-switched communication networks may be considered as optimization problem, more precisely, as a shortest-path problem. The Hopfield-type neural network is a very efficient tool for solving such problems. The suggested routing algorithm is designed to find the optimal path, meaning, the shortest path (if possible), but taking into account the traffic conditions: the incoming traffic flow, routers occupancy, and link capacities, avoiding the packet loss due to the input buffer overflow. The applicability of the proposed model is demonstrated through computer simulations in different traffic conditions and for different full-connected networks with both symmetrical and non-symmetrical links.