Comparative Analysis of Distributive Optimized Clustering Techniques in Cognitive Radio Networks

In this paper we study and compare the performance of Distributed Firefly Optimized Clustering (DFOC) with Distributed Swarm Optimized Clustering (DSOC) optimization techniques used for the dynamic clustering. Proposed Distributed Firefly Optimized Clustering (DFOC) is an optimization algorithm based on the function of attractiveness of firefly behavior. All the cognitive nodes move towards the brighter firefly with random velocity to form an organized cluster with least computation time. In the existing DSOC method each particle’s best position and velocity are evaluated according to the objective function until an optimum global best position is reached. The convergence rate of DSOC is similar to Genetic Algorithm (GA). The proposed DFOC, the SU power is reduced to 7.34% for 100 numbers of SUs.compared to DSOC.