Hybridization of water wave optimization and sequential quadratic programming for cognitive radio system

Nature-inspired algorithms are attracting attention of researchers due to their simplicity and flexibility. These algorithms are analyzed in terms of their key features like their diversity and adaptation, exploration and exploitation, as well as attraction and diffusion mechanisms. Every optimization algorithm needs to address the exploration and exploitation of a search space. In order to be successful, these algorithms need to establish a good ratio between exploration and exploitation. In this paper, water wave optimization (WWO) algorithm is integrated with sequential quadratic programming (SQP) called WWO–SQP for solving constrained high-dimensional problems. This new hybrid algorithm is able to explore globally through WWO and exploit locally through SQP to speed up the search process to find the best solution. The proposed hybrid algorithm is applied on cognitive radio (CR) system to optimize the allocation of frequency spectrum. This is done by sensing the various radio frequency parameters from the environment to the users on their demand. The reliability and efficiency of WWO–SQP algorithm are checked by using benchmark functions. In the optimization of CR system, the results obtained by the proposed algorithm are compared with various optimization algorithms. The results show that WWO–SQP has high accuracy, stability and outperforms other competitive algorithms.