Multi-Objective Task Offloading Optimization in Mobile Edge Computing: A Comprehensive Survey of Evolutionary Algorithms and UAV-Assisted Approaches

Mobile Edge Computing (MEC) has become a cornerstone paradigm for supporting computation-intensive and latency-critical applications in beyond-۵G/۶G networks. Task offloading in MEC requires simultaneous optimization of conflicting objectives such as end-to-end delay, energy consumption, system reliability, and operational cost. This paper presents a systematic literature review (SLR) of multi-objective task offloading strategies with special emphasis on evolutionary and meta-heuristic algorithms as well as UAV-assisted MEC systems published between ۲۰۲۱ and ۲۰۲۵. The reviewed mechanisms are classified according to algorithmic foundation, problem modeling, and deployment scenario. Performance achievements, limitations, and research gaps are critically analyzed to provide clear guidelines for future studies.

کلیدواژه ها:

Mobile Edge Computing ، Multi-objective optimization ، Task offloading ، Evolutionary algorithms ، UAV-assisted MEC ، Pareto optimization ، Energy-delay trade-off