Evaluation of neural and entropy-constrained routing of communication networks

This paper presents the results of a study that compared the performance of an entropy-constrained routing algorithm with that of routing methods based on neural optimization. The entropy-constrained algorithm was developed to allow multiple nodes of a communication network to compete for each position of a route and it is implemented as a deterministic annealing process. Routing is also performed by the routron and by a routing method relying on the Hopfield-Tank approach to optimization. This experimental study reveals the superiority of the entropy-constrained routing algorithm, which produces consistently the best routes in a small fraction of the time required for route discovery by the neural optimization methods.