Genetic algorithms with elitism-based immigrants for dynamic load balanced clustering problem in mobile ad hoc networks

Clustering can help aggregate the topology information and reduce the size of routing tables in a mobile ad hoc network (MANET). To achieve fairness and even energy consumption, each clusterhead should ideally support the same number of cluster members. Moreover, one of the most important characteri...

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Vydáno v:2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments s. 1 - 7
Hlavní autoři: Hui Cheng, Shengxiang Yang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.04.2011
Témata:
Biological cells
Clustering algorithms
dynamic clustering
elitism-based immigrants
Genetic algorithms
Heuristic algorithms
Mobile ad hoc networks
Network topology
Topology
ISBN:1424499305, 9781424499304
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Shrnutí:Clustering can help aggregate the topology information and reduce the size of routing tables in a mobile ad hoc network (MANET). To achieve fairness and even energy consumption, each clusterhead should ideally support the same number of cluster members. Moreover, one of the most important characteristics in MANETs is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, for a dynamic and complex system like MANET, an effective clustering algorithm should efficiently adapt to each topology change and produce the new load balanced solution quickly. The maintenance of the cluster structure should be as stable as possible to reduce overhead. It requires that the new solution should try to keep most of the good parts in the previous solution. In this paper, we propose to use elitism-based immigrants genetic algorithm (EIGA) to solve the dynamic load balanced clustering problem in MANETs. Each individual represents a feasible clustering structure and its fitness is evaluated based on the load balance metric. Immigrants are introduced to help the population to handle the topology dynamics and produce new and closely related solutions. The experimental results show that EIGA can quickly adapt to the environmental changes (i.e., the network topology change) and produce high-quality solutions after each change.
ISBN:1424499305
9781424499304
DOI:10.1109/CIDUE.2011.5948486