Multi-cognitive network resource allocation based on improved artificial bee colony algorithm
Lu Li, Luyong Zhang · Journal of Physics Conference Series · 2020
Abstract This paper introduces a graph theory model for resource allocation in multi-cognitive wireless network scenarios. Aiming at the problems of low search accuracy and slow convergence speed of the basic artificial bee colony algorithm, an improved bee colony algorithm is proposed. The improved algorithm introduces an adaptive t-distribution mutation strategy. Compared with the original algorithm, the performance of the improved algorithm has greatly improved. At the same time, the improved bee colony algorithm is applied to multi-cognitive wireless network scenarios for resource allocation in the model. The experimental results show that the improved artificial bee colony algorithm can obtain greater system throughput and effectively reduce the performance of multi-cognitive wireless network systems, and can allocate resources more reasonably.