Prediction of stability of the clusters in Manet using Genetic Algorithm

Genetic Algorithm is one of the powerful tool to constitute search and optimization procedures. It can optimize either continuous or discrete inputs. Among the metaheuristics currently available, evolutionary algorithm can help to find the best function with limited known data available. Clustering in mobile ad hoc network gained importance among researchers for easier maintenance of network. Clustering will be suitable for effective communication of group networks such as disaster relief networks and so on. The problem in clustering of Manet is the frequent cluster re-affiliation and lack of stability of the network. In this paper, we propose an approach to find the stability of the network using Genetic algorithm based on the average number of clusters, load balancing factors and weighted parameters. The best objective function for the stability of the network is ranked using Genetic Algorithm. Experimental results shows that the stability of the network can be maximized by determining the average number of clusters formed during transmission and load balancing factor of the cluster head and the weighted parameters determined priorly before transmission.