An iterative algorithm for delay-constrained minimum-cost multicasting
IEEE/ACM Transactions on Networking (TON)
Ad Hoc Wireless Networks: Protocols and Systems
Ad Hoc Wireless Networks: Protocols and Systems
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
A Comparative Study of Steady State and Generational Genetic Algorithms
Selected Papers from AISB Workshop on Evolutionary Computing
Ad Hoc Wireless Networks: Architectures and Protocols
Ad Hoc Wireless Networks: Architectures and Protocols
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A self-organizing random immigrants genetic algorithm for dynamic optimization problems
Genetic Programming and Evolvable Machines
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
An oriented spanning tree based genetic algorithm for multi-criteria shortest path problems
Applied Soft Computing
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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.