An Efficient Opposition Based Grey Wolf Optimizer for Weight Adaptation in Cooperative Spectrum Sensing

This paper presents an integrated meta-heuristic technique, namely opposition based grey wolf optimizer (OBGWO) and demonstrates its application for optimizing the sensing performance of cooperative spectrum sensing (CSS) scheme in cognitive radio (CR) system. The proposed technique improves the search ability of grey wolf optimizer (GWO) by integrating it with the concept of opposition based learning. Further, the competence of OBGWO is tested on seven benchmark functions and its performance is compared with other existing meta-heuristic techniques. Simulation results demonstrate that OBGWO provides better solutions and improved convergence characteristics when compared with GWO, sine–cosine algorithm and moth flame optimization algorithm. Subsequently, the proposed scheme when applied to weight vector optimization for CSS; results in higher probability of detection for a given probability of false alarm.