An improved monarch butterfly spectrum allocation algorithm for multi-source data stream in complex electromagnetic environment

Abstract In the era of Internet Everything, various wireless devices and sensors use spectrum, which is a precious non-renewable resource, to communication. Due characteristics massive, heterogeneous, multi-source, generated multi-source data stream brings difficulties spectrum cognition. As result, unreasonable allocation strategy leads low utilization resources. Optimizing can effectively improve utilization. Aiming at problem trapped local optimum solution in genetic algorithm (GA) particle swarm optimization (PSO), an improved monarch butterfly proposed. Firstly, this paper employs simulated annealing select migration rate, increases diversity population. Secondly, chaos mapping utilized ability convergence speed. Finally, view that easy fall into optimal solution, there no better way escape from solution. The Wolf pack updating operator selected population generate new butterflies. This method updates by generating individuals, so as increasing experimental results show outperforms other two algorithms terms speed system revenue.