基于改进人工蜂鸟算法的MEC任务卸载策略OA北大核心CSTPCD

Considering the computing requirements of a large number of delay-sensitive and computation-intensive tasks in the information network environment.Mobile Edge Computing(MEC)and its computation offloading technology provide an effective solution.Therefore,a cost optimization algorithm is designed for task offloading strategies in resource-constrained mobile edge systems.First,a multi-user and multi-server network scenario is constructed based on the basic data structure of the system,and a minimum cost optimization model,including penalty terms,is established based on optimization indicators such as latency and energy consumption.An Improved Artificial Hummingbird Algorithm(IAHA)is further proposed to adaptively adjust and optimize the structure and optimization method of the original algorithm,and an emergency avoidance strategy is introduced to achieve a high degree of fit between the system model and algorithm mapping,thereby providing a fast and accurate solution to the model problem and obtaining the optimal offloading strategy for the system.Finally,the application strategy is deployed to reduce system costs and enhance user service experience.The simulation results show that the proposed improved algorithm can effectively reduce system costs and has outstanding convergence performance and optimization accuracy when solving high-dimensional complex models.Under specific experimental conditions,this improved algorithm reduced system costs by 20.79％to 65.39％,respectively,compared with some classic metaheuristic and typical new swarm intelligence algorithms,and the average system cost is 66.98％less than those of local computing strategies with the proposed task offloading algorithm.