Resource optimizer for Cognitive Network using multi-objective particle swarm system

Abstract

Recently, Cognitive network has drawn the attention as a promising technology to enhance communication system performance by efficiently utilizing system resources. It provides prompt response to dynamic changes. In this paper, a modified multi-objective particle swarm optimization (M-MOPSO) is proposed in Cognitive IP Multimedia Subsystem (CogIMS) to improve the global network performance. The implementation and evaluation results of the system design using the algorithm is provided and compared with those obtained using Non-Dominated Sorting Genetic Algorithm (NSGA-II). Extensive simulations are carried out by using MATLAB software showed that M-MOPSO is comparable to NSGA-II in the network throughput. However, on average, M-MOPSO is faster than NSGA-II by 6.25 times considering the needed computation time for algorithm convergence.