Aiming　at　the　problem　that　mobile　terminal　(MT)　harvests　less　energy　from　ambient　radio　frequency　(RF)　sources,　the　resource　allocation　strategy　in　mobile　edge　computing　(MEC)　system　with　hybrid　energy　harvesting　is　investigated　in　this　paper.　By　deploying　multiple　magnetic　induction　energy　quick　charging　stations　(MI-CSs)　within　the　coverage　area　of　the　base　station,　the　MT　can　supplement　extra　energy　at　a　nearby　MI-CS　when　the　energy　harvested　from　ambient　RF　sources　is　about　to　be　exhausted.　The　MT　offloads　computing　task　to　edge　server　by　leveraging　MEC　technology.　The　resource　allocation　problem　is　formulated　as　an　optimization　problem.　The　objective　is　to　minimize　the　total　energy　consumption　of　MTs　under　the　constraints　of　computing　capability　range　of　MT,　maximal　computing　resource　of　edge　server,　computing　delay　of　task,　and　battery　energy　of　MT.　The　suboptimal　solution　is　obtained　by　adopting　the　quantum-behaved　particle　swarm　optimization　(QPSO)　algorithm.　Simulation　results　show　that　the　QPSO　algorithm　has　less　energy　consumption　compared　with　the　standard　particle　swarm　optimization　algorithm　and　the　fixed　computing　resource　allocation　method.　©　2020　IEEE.