Energy and Makespan Bi-Objective Optimization for UAV-Assisted MEC Task Offloading

In the framework of unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC), the UAV’s trajectory design is crucial for the effectiveness of task offloading. However, existing studies suffer from some limitations such as reliance on static decision metrics, prediction-based strategies with poor generalization, or high computational complexity, making them unsuitable for large-scale emergency scenarios with surging task demands. To address this issue, this study introduces the Location Priority Level (LPL) and proposes LPL-based Bi-objective Task Offloading Strategy (LBTOS), which tries to simultaneously minimize task completion time (makespan) and total energy consumption. By jointly considering task load, regional deadlines, and UAV energy constraints, LBTOS introduces the LPL to dynamically map task urgency to UAV trajectories. Leveraging LPL and an adaptive triggering mechanism, the proposed UAV Trajectory Design algorithm (UAVTD) achieves high responsiveness under computationally constrained emergency scenarios. Additionally, a task offloading algorithm named HWPSO-SA is designed, which integrates adaptive and time-varying inertia weights, and combines the Particle Swarm Optimization with Simulated Annealing, thereby enhancing the algorithm’s capability to obtain the global optimal solution. Lastly, simulation experiments are conducted to validate the significant effectiveness of LBTOS in the dual optimization objectives of minimizing task completion time and the total computing and transmission energy consumption of the system.