To　address　the　problem　that　user　device　(UD)　harvests　less　energy　from　environmental　radio　frequency　(RF)　sources,　a　resource　allocation　strategy　in　dual　unmanned　aerial　vehicles　(UAVs)-assisted　mobile　edge　computing　(MEC)　system　with　hybrid　energy　harvesting　is　studied　in　this　paper.　By　deploying　two　UAVs　with　hybrid　solar　and　RF　energy　harvesting,　they　can　provide　edge　computing　services　for　the　UDs　and　supply　energy　for　the　low　energy　UDs,　respectively.　When　the　UD　has　a　large　computing　task,　the　computing　task　can　be　offloaded　to　the　MEC　server　carried　by　the　UAV.　The　computing　pressure　of　this　UD　can　be　greatly　reduced.　If　the　energy　harvested　by　the　UD　from　the　ambient　RF　sources　is　not　enough,　another　UAV　flies　close　to　this　UD.　The　UAV　acts　as　a　dedicated　RF　source　and　transfers　energy　to　the　UD.　The　problem　of　resource　allocation　is　formulated　as　a　mixed-integer　nonlinear　programming　problem　by　jointly　considering　the　energy　consumed　by　UDs　and　UAVs.　The　objective　is　to　minimize　the　total　energy　consumption　under　the　constraints　of　computing　capacity　and　energy　consumption　of　MDs　and　UAVs.　The　suboptimal　solution　can　be　obtained　by　introducing　a　quantum-behaved　particle　swarm　optimization　(QPSO)　algorithm.　Simulation　results　verify　that　the　proposed　QPSO　algorithm　has　lower　energy　consumption　in　contrast　to　the　other　traditional　schemes.　©　2021　IEEE.