Genetic algorithms with hyper-mutation 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 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. In this paper, we propose to use two types of hyper-mutation genetic algorithms (GAs) to solve the dynamic load balanced clustering problem in MANETs. In the GA population, each individual represents a feasible clustering structure and its fitness is evaluated based on the load balance metric. The two GAs are named as hlHMGA and grHMGA, respectively. The experimental results show that both algorithms work well for the problem when appropriate parameters are identified and that hlHMGA outperforms grHMGA.