An　edge　cloud　architecture　plays　a　key　role　in　improving　the　user　task　computing　service　system　by　combining　the　powerful　data　processing　capability　of　cloud　centres　with　the　low　latency　of　edge　computing.　Existing　methods　for　maximizing　the　efficiency　of　an　edge　cloud　architecture　take　into　account　time　and　task　parameters　but　ignore　other　factors　such　as　load　balancing,　cost,　and　user　satisfaction　when　scheduling　resources.　In　this　work,　we　propose　a　many-objective　resource　scheduling　model　for　optimizing　the　performance　of　an　edge　cloud　architecture,　which　takes　into　account　the　time　spent　on　task,　cost,　load　balance,　user　satisfaction,　and　trust　measurement.　The　resource　scheduling　model　converges　to　the　optimal　solution　using　a　novel　many-objective　ensemble　optimization　algorithm　based　on　a　dynamic　selection　mechanism.　The　study　also　explores　the　support　set　convergence　of　eight　evolutionary　operators　using　the　ensemble　algorithm.　The　model　solutions　are　dynamically　updated　with　the　help　of　the　dynamic　integration　probability,　and　then　a　selection　criteria　is　used　to　pick　the　best　solutions　from　the　pool　of　generated　solutions.　Two　simulations　on　a　benchmark　dataset　are　used　to　verify　the　usefulness　and　performance　of　the　designed　algorithm.　Our　approach　was　able　to　locate　more　than　half　of　the　best　solutions　on　the　benchmark　functions,　and　it　also　showed　to　be　a　better　model　solution　than　the　some　of　the　popular　many-objective　algorithms　for　dealing　with　the　edge　cloud　resource　scheduling　problem,　according　to　the　results　obtained　from　the　simulations.　IEEE