  Recent developments in unmanned aerial vehicles (UAVs) and mobile edge
computing (MEC) have provided users with flexible and resilient computing
services. However, meeting the computing-intensive and latency-sensitive
demands of users poses a significant challenge due to the limited resources of
UAVs. To address this challenge, we present a multi-objective optimization
approach for multi-UAV-assisted MEC systems. First, we formulate a
multi-objective optimization problem \textcolor{b2}{aiming} at minimizing the
total task completion delay, reducing the total UAV energy consumption, and
maximizing the total amount of offloaded tasks by jointly optimizing task
offloading, computation resource allocation, and UAV trajectory control. Since
the problem is a mixed-integer non-linear programming (MINLP) and NP-hard
problem which is challenging, we propose a joint task offloading, computation
resource allocation, and UAV trajectory control (JTORATC) approach to solve the
problem. \textcolor{b3}{However, since the decision variables of task
offloading, computation resource allocation, and UAV trajectory control are
coupled with each other, the original problem is split into three sub-problems,
i.e., task offloading, computation resource allocation, and UAV trajectory
control, which are solved individually to obtain the corresponding decisions.}
\textcolor{b2}{Moreover, the sub-problem of task offloading is solved by using
distributed splitting and threshold rounding methods, the sub-problem of
computation resource allocation is solved by adopting the Karush-Kuhn-Tucker
(KKT) method, and the sub-problem of UAV trajectory control is solved by
employing the successive convex approximation (SCA) method.} Simulation results
show that the proposed JTORATC has superior performance compared to the other
benchmark methods.
