Time-Saving Deployment Optimization Algorithm Based on the Number of Adaptive Drones
To reduce the average delay required by Mobile Edge Computing(MEC)service users in unknown environments and to improve the Quality of Service(QoS)of MEC systems,this study designs both an MEC system based on multiple Unmanned Aerial Vehicles(UAVs)and a time-saving deployment algorithm with a variable number of UAVs.The MEC system first decomposes the UAV deployment problem into a two-layered nesting problem.The outer layer is a deployment coverage problem based on the Maximum Covering Location Problem(MCLP),and the inner layer is a task offloading problem based on the General Assignment Problem(GAP).Artificially set penalty terms are added to the target to be optimized to achieve a balance between the number of UAVs in the MEC system and the average latency required by users during the optimization process.As the deployment algorithm,the study designs a hybrid algorithm to solve the nesting problem.The outer layer uses a joint optimization algorithm based on Differential Evolution-Snake Optimization(DE-SO)to solve the UAV deployment coverage problem,whereas the inner layer uses a greedy algorithm to solve the task offloading problem.Several simulation experiments show that,compared with CS-G,SAO-G,and other algorithms,the proposed algorithm achieves the best performance in terms of fitness,coverage,and other performance under a variety of User Equipment(UE)distribution environments.In addition,compared with the comparison algorithm with the highest optimization accuracy,DE-SO-G improves the optimization accuracy by an average of 5.67％.
Mobile Edge Computing(MEC)UAV deploymentsnake optimizer algorithmdifferential evolution algorithmmixed integer nonlinear problem
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