Message Routing and Scheduling in Optical Multistage Networks using Bayesian Inference method on AI algorithms

Optical multistage interconnection networks (MINs) suffer from optical-loss during switching and crosstalk problem in the switches. The crosstalk problem is solved by routing messages using time division multiplexing (TDM) approach. This paper focuses on minimizing the number of groups (time slots) required to realize a permutation. Many researchers concentrated on this NP-hard problem and concluded that AI algorithms perform better than the heuristic algorithms. They also showed that majority of the times the performance of genetic algorithm (GA) was better than simulated annealing algorithm (SAA). In this research, we implement a new approach to minimize the number of passes required for scheduling a given permutation. A combinational method is developed which comprises the use of Bayesian inference method on GA and SAA to always guarantee the best solution, instead of only using either GA or SAA. Simulations are performed in Java using multiple threads to run SA and GAA in parallel and to evaluate the performance of the new method. The results are then compared to those obtained from GA and SAA.