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Dynamic Genetic Algorithms with Hyper-Mutation Schemes for Dynamic Shortest Path Routing Problem in Mobile Ad Hoc Networks

Dynamic Genetic Algorithms with Hyper-Mutation Schemes for Dynamic Shortest Path Routing Problem in Mobile Ad Hoc Networks

Hui Cheng
Copyright: © 2012 |Volume: 3 |Issue: 1 |Pages: 12
ISSN: 1947-9220|EISSN: 1947-9239|EISBN13: 9781466610491|DOI: 10.4018/jaras.2012010105
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MLA

Cheng, Hui. "Dynamic Genetic Algorithms with Hyper-Mutation Schemes for Dynamic Shortest Path Routing Problem in Mobile Ad Hoc Networks." IJARAS vol.3, no.1 2012: pp.87-98. http://doi.org/10.4018/jaras.2012010105

APA

Cheng, H. (2012). Dynamic Genetic Algorithms with Hyper-Mutation Schemes for Dynamic Shortest Path Routing Problem in Mobile Ad Hoc Networks. International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS), 3(1), 87-98. http://doi.org/10.4018/jaras.2012010105

Chicago

Cheng, Hui. "Dynamic Genetic Algorithms with Hyper-Mutation Schemes for Dynamic Shortest Path Routing Problem in Mobile Ad Hoc Networks," International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS) 3, no.1: 87-98. http://doi.org/10.4018/jaras.2012010105

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Abstract

In recent years, the static shortest path (SP) routing 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 mesh network (WMN), 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 routing problem in MANETs turns out to be a dynamic optimization problem. This paper proposes to use two types of hyper-mutation GAs to solve the dynamic SP routing problem in MANETs. The authors consider MANETs as target systems because they represent new generation wireless networks. The experimental results show that the two hyper-mutation GAs can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.

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