A Coarse-Grained Parallel Genetic Algorithm with Migration for Shortest Path Routing Problem

Shortest path routing is the type of routing widely used in computer networks nowadays. Even though shortest path routing algorithms are well established, other alternative methods may have their own advantages. One such alternative is to use a GA-based routing algorithm. Based on previous research, GA-based routing algorithm has been found to be more scalable and insensitive to variations in network topologies. However, it is also known that GA-based routing algorithm is not fast enough for real-time computation. In this paper, we proposed a coarse-grained parallel genetic algorithm for solving the shortest path routing problem with the aim to reduce its computation time. The migration scheme, which is commonly used in coarse-grained parallel genetic algorithm, is also employed in the proposed algorithm. This algorithm is developed and run on an MPI cluster. This paper studies the effect of migration on the proposed algorithm and the performance of the algorithm as compared to its serial counterpart.