In　multi-objective　and　many-objective　optimization,　weight　vectors　are　particularly　crucial　to　the　performance　of　decomposition-based　optimization　algorithms.　The　uniform　weight　vectors　are　not　suitable　for　complex　Pareto　fronts　(PFs),　so　it　is　necessary　to　improve　the　distribution　of　weight　vectors.　Besides,　the　balance　between　convergence　and　diversity　is　a　difficult　issue　as　well　in　multi-objective　optimization,　and　it　becomes　increasingly　important　with　the　augment　of　the　number　of　objectives.　To　address　these　issues,　a　self-adaptive　weight　vector　adjustment　strategy　for　decomposition-based　multi-objective　differential　evolution　algorithm　(AWDMODE)　is　proposed.　In　order　to　ensure　that　the　guidance　of　weight　vectors　becomes　accurate　and　effective,　the　adaptive　adjustment　strategy　is　introduced.　This　strategy　distinguishes　the　shapes　and　adjusts　weight　vectors　dynamically,　which　can　ensure　that　the　guidance　of　weight　vectors　becomes　accurate　and　effective.　In　addition,　a　self-learning　strategy　is　adopted　to　produce　more　non-dominated　solutions　and　balance　the　convergence　and　diversity.　The　experimental　results　indicate　that　AWDMODE　outperforms　the　compared　algorithms　on　WFG　suites　test　instances,　and　shows　a　great　potential　when　handling　the　problems　whose　PFs　are　scaled　with　different　ranges　in　each　objective.