Spectrum Allocation Algorithm Based on Improved Chimp Optimization Algorithm

In response to the rapid growth of spectrum resource demand and low utilization of spectrum, this paper proposes a spectrum allocation solution based on improved chimp optimization algorithm (ICHOA). First, opposition-based learning is applied to improve the quality of the initial solution by generating opposing solutions; Next, a proportional weight-based location update method is used to dynamically adjust the location update vector to play the leading role of the attacking chimp. Then, a nonlinear convergence factor is used for spatial search to enhance the local exploration ability and global exploitation ability; Finally, the improved algorithm is combined with the spectrum allocation model to maximize the efficiency of the system. Compared with genetic algorithm (GA), improved particle swarm optimization algorithm (IPSO), improved gray wolf algorithm (IGWO) and traditional chimp optimization algorithm (CHOA), the simulation results show that ICHOA algorithm has faster convergence speed and higher system benefits in spectrum allocation. It effectively improves the utilization rate of spectrum resources and the proportion fairness of cognitive users.