Evolutionary approach for message scheduling in optical Omega networks

Optimal routing in optical Omega network is an NP-hard problem and traditional heuristics have only limited success in solving small to midsize routing problems. In this paper, we explore the possibility of using genetic algorithm (GA) to optimize a routing solution on optical Omega networks, and determine the impact of various factors, specifically the impact of crossover probability, mutation probability, and population size on GA´s performance. We use different operators and parameters of GA to test their impact on the performance of the algorithm and obtain a good range for each parameter. To compare the performance of the GA to other existing heuristic routing algorithms, many cases are tested and the results are analyzed. The results indicate that the genetic algorithm can reduce the number of passes to send messages on an optical Omega network without crosswalk.