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Routing in Random Multistage Interconnections Networks: Comparing Exhaustive Search, Greedy and Neural Network Approaches

The problem of establishing point-to-point communication routes in a random multistage interconnection network (RMIN) is addressed. A neural network routing scheme is presented. This routing scheme is compared to two more traditional routing techniques—namely, exhaustive search routing and greedy routing. The main criterion that is examined is the ability of each routing methodology to solve routing problems. Results are obtained through simulation of the routing methodologies for three different RMINs. The sample RMINs are relatively small since the neural network router in its present form will only be competitive for small RMINs. The simulations show that the three routing schemes perform similarly for the three sample RMINs. Other criteria that will be touched upon are the speed and the resource utilization of each routing methodology and the pros and cons of each approach will be discussed. The results suggest that neural network routers may be appropriate for some communication applications involving RMINs.