In　real-world,　there　exist　lots　of　many-objective　optimization　problems　(MaOPs),　which　severely　challenge　well-known　multi-objective　evolutioanry　algorithms　(MOEAs).　A　many-obective　evolutioanry　algorithm　combining　decomposition　and　coevolution　(MaOEA/DCE)　is　presented　in　this　paper.　MaOEA/DCE　adopts　mix-level　orthogonal　experimental　design　to　produce　a　set　of　weight　vectors　evenly　distributed　in　weight　coefficient　space,　so　as　to　improve　the　diversity　of　initial　population.　In　addition,　the　MaOEA/DCE　integrates　differential　evolution　(DE)　with　the　adaptive　SBX　operator　to　generate　high-quality　offspring　for　enhancing　the　convergence　of　evolutionary　population.　Some　comparative　experiments　are　conducted　among　MaOEA/DCE　and　other　five　representative　MOEAs　to　examine　their　IGD+　performance　on　four　MaOPs　of　DTLZ{1,　2,　4,　5}.　The　experimental　results　show　that　the　proposed　MaOEA/DCE　has　overall　performance　advantage　over　the　other　peering　MOEAs　in　terms　of　convergence,　diversity,　and　robustness.　©　Copyright　2020,　Institute　of　Software,　the　Chinese　Academy　of　Sciences.　All　rights　reserved.