Genetic algorithms with elitism-based immigrants for dynamic shortest path problem in mobile ad hoc networks.
In recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks (ANNs), genetic algorithms (GAs), particle swarm optimization (PSO), etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless sensor network (WSN), etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, the SP problem turns out to be a dynamic optimization problem (DOP) in MANETs. In this paper, we propose to use elitism-based immigrants GA (EIGA) to solve the dynamic SP problem in MANETs. We consider MANETs as target systems because they represent new generation wireless networks. The experimental results show that the EIGA can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.
Citation:Cheng, H. and Yang, S. (2009) Genetic algorithms with elitism-based immigrants for dynamic shortest path problem in mobile ad hoc networks. In: Proceedings of the 2009 IEEE Congress on Evolutionary Computation, Trondheim, 2009. New York: IEEE, pp. 3135-3140.
Research Group:Centre for Computational Intelligence