DocumentCode :
1643526
Title :
Genetic algorithms with elitism-based immigrants for dynamic shortest path problem in mobile ad hoc networks
Author :
Cheng, Hui ; Yang, Shengxiang
Author_Institution :
Dept. of Comput. Sci., Univ. of Leicester, Leicester
fYear :
2009
Firstpage :
3135
Lastpage :
3140
Abstract :
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.
Keywords :
ad hoc networks; genetic algorithms; mobile computing; telecommunication network topology; dynamic optimization problem; elitism-based immigrants; genetic algorithms; intelligent optimization; mobile ad hoc networks; network topology; shortest path problem; wireless communications; Artificial intelligence; Artificial neural networks; Genetic algorithms; Intelligent networks; Intelligent sensors; Mobile ad hoc networks; Network topology; Particle swarm optimization; Shortest path problem; Wireless sensor networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2009. CEC '09. IEEE Congress on
Conference_Location :
Trondheim
Print_ISBN :
978-1-4244-2958-5
Electronic_ISBN :
978-1-4244-2959-2
Type :
conf
DOI :
10.1109/CEC.2009.4983340
Filename :
4983340
Link To Document :
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