Spectrum Allocation Based on Spider Monkey Optimization Algorithm with Nonlinear Inertia Weight and Sine-Cosine Algorithm

To improve spectrum allocation optimization and optimal convergence accuracy in cognitive radio, a nonlinear spider monkey algorithm based on sine-cosine Algorithm (SCNWSMO) is proposed. In the decision-making stages of global leader and the local leader, the spider monkey individuals are optimized by sine-cosine Algorithm. Moreover, the nonlinear decreasing inertia weight factor is introduced to effectively control the global optimization and local optimization capabilities of the algorithm and improve the convergence speed. Finally, the performance of SCNWSMO is compared with various algorithms total system benefit, and average network benefit. Simulation results show that the SCNWSMO is advantageous over other algorithms with higher network efficiency.