An iterative algorithm for delay-constrained minimum-cost multicasting
IEEE/ACM Transactions on Networking (TON)
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Forking genetic algorithms: Gas with search space division schemes
Evolutionary Computation
Solving shortest path problem using particle swarm optimization
Applied Soft Computing
Genetic algorithms with memory-and elitism-based immigrants in dynamic environments
Evolutionary Computation
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
A genetic algorithm for shortest path routing problem and the sizing of populations
IEEE Transactions on Evolutionary Computation
Population-Based Incremental Learning With Associative Memory for Dynamic Environments
IEEE Transactions on Evolutionary Computation
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The static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless mesh network, 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 in mobile wireless networks. In this paper, we propose to use multi-population GAs with immigrants scheme to solve the dynamic SP problem in MANETs which is the representative of new generation wireless networks. The experimental results show that the proposed GAs can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.