With　the　increasing　application　of　unmanned　aerial　vehicles　(UAVs),　UAV-based　base　stations　(BSs)　have　been　widely　used.　In　some　situations　when　there　is　no　ground　BSs,　such　as　mountainous　areas　and　isolated　islands,　or　BSs　being　out　of　service,　like　disaster　areas,　UAV-based　networks　may　be　rapidly　deployed.　In　this　paper,　we　propose　a　framework　for　UAV　deployment,　power　control,　and　channel　allocation　for　device-to-device　(D2D)　users,　which　is　used　for　the　underlying　D2D　communication　in　UAV-based　networks.　Firstly,　the　number　and　location　of　UAVs　are　iteratively　optimized　by　the　particle　swarm　optimization-　(PSO-)　Kmeans　algorithm.　After　UAV　deployment,　this　study　maximizes　the　energy　efficiency　(EE)　of　D2D　pairs　while　ensuring　the　quality　of　service　(QoS).　To　solve　this　optimization　problem,　the　adaptive　mutation　salp　swarm　algorithm　(AMSSA)　is　proposed,　which　adopts　the　population　variation　strategy,　the　dynamic　leader-follower　numbers,　and　position　update,　as　well　as　Q-learning　strategy.　Finally,　simulation　results　show　that　the　PSO-Kmeans　algorithm　can　achieve　better　communication　quality　of　cellular　users　(CUEs)　with　fewer　UAVs　compared　with　the　PSO　algorithm.　The　AMSSA　has　excellent　global　searching　ability　and　local　mining　ability,　which　is　not　only　superior　to　other　benchmark　schemes　but　also　closer　to　the　optimal　performance　of　D2D　pairs　in　terms　of　EE.