Edge　computing　offers　a　groundbreaking　architecture　for　supplying　computing,　storage,　and　networking　resources　to　propel　the　Internet　of　Things　forward.　By　situating　them　at　the　network＇s　edge,　this　model　makes　computational　power　more　accessible　to　users.　If　tasks　are　executed　entirely　at　the　edge,　energy　and　resource　constraints　of　edge　nodes　may　lead　to　poor　performance.　Therefore,　it　is　widely　recognized　that　offloading　certain　tasks　to　cloud　data　centers　(CDCs),　which　possess　abundant　execution　resources,　is　advantageous.　However,　implementing　CDCs　is　not　widespread　and　lacks　flexibility　in　isolated　regions.　This　presents　challenges　and　high　costs　for　reliably　completing　tasks　quickly.　Consequently,　employing　more　adaptable　unmanned　aerial　vehicles　(UAVs)　as　CDCs　in　specific　scenarios　is　crucial.　The　work　presents　the　idea　of　mobile　edge　computing　supported　by　the　UAV.　By　considering　the　needs　of　user　services,　we　enhance　the　energy　efficiency　of　the　UAV　by　optimizing　their　trajectories,　transmission　power,　and　computational　load　distribution.　Furthermore,　the　work　introduces　an　improved　algorithm　called　GeneticSimulated-annealing-based　Particle　Swarm　Optimizer　(GSPSO)　to　optimize　the　energy　efficiency　of　the　UAV.　Experimental　simulations　show　that　regarding　the　energy　efficiency　of　the　UAV,　GSPSO　exhibits　superior　search　efficiency,　surpassing　genetic　algorithm,　simulated　annealing,　and　particle　swarm　optimization　by　7.39%,　15.03%,　and　27.93%,　respectively.　©　2024　IEEE.