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Title: Genetic algorithms with elitism-based immigrants for dynamic load balanced clustering problem in mobile ad hoc networks
Authors: Cheng, H
Yang, S
Keywords: Mobile ad hoc networks;Dynamic clustering;Elitism-based immigrants
Issue Date: 2011
Publisher: IEEE
Citation: IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), Paris: 1 - 7, 11-15 Jul 2011
Abstract: In recent years, 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 networks [mobile ad hoc networks (MANETs)], wireless sensor networks, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, i.e., 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. In this paper, we propose to use GAs with immigrants and memory schemes to solve the dynamic SP routing problem in MANETs. We consider MANETs as target systems because they represent new-generation wireless networks. The experimental results show that these immigrants and memory-based GAs can quickly adapt to environmental changes (i.e., the network topology changes) and produce high-quality solutions after each change.
Description: This article is posted here with permission of IEEE - Copyright @ 2011 IEEE
ISBN: 978-1-4244-9930-4
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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