Mobile　edge　computing　(MEC)　emerges　as　a　vital　paradigm　to　support　the　increasing　use　of　mobile　users　(MUs)　with　capabilities　similar　to　cloud　computing.　While　most　research　concentrates　on　MEC　facilitated　by　terrestrial　base　stations　(BSs),　its　applicability　in　scenarios　such　as　disaster　rescue　and　field　operations　is　limited.　Efforts　have　been　made　to　explore　MEC　assisted　by　unmanned　aerial　vehicles　(UAVs)　with　efficient　scheduling　algorithms.　However,　relying　solely　on　UAVs　for　MEC　has　limitations,　particularly　for　computation-intensive　applications.　This　work　proposes　a　hybrid　MEC　system　lever-aging　UAVs　and　BS.　Multiple　UAVs　and　a　BS　are　deployed　to　provide　MEC　services　directly　from　UAVs　or　indirectly　from　the　BS.　We　formulate　an　energy-efficient　scheduling　problem　to　minimize　energy　consumption　by　jointly　optimizing　UAV　trajectories,　task　associations,　and　allocation　of　computing　and　transmitting　resources.　To　solve　it,　this　work　designs　a　hybrid　algorithm　named　_S_uccess　History-based　parameter　Adaptation　for　Differential　volution　with　a　Niching-based　population　size　reduction　strategy　and　an　efficient　nsemble　sinusoidal　scheme　(SHADE-NE).　Experimental　results　validate　the　superiority　of　SHADE-NE　over　its　benchmark　peers,　thus　proving　that　SHADE-NE　greatly　enhances　the　performance　of　the　system.　©　2024　IEEE.