In　the　past　two　decades,　multi-objective　evolutionary　algorithms　(MOEAs)　have　achieved　great　success　in　solving　two　or　three　multi-objective　optimization　problems.　As　pointed　out　in　some　recent　studies,　however,　MOEAs　face　many　difficulties　when　dealing　with　many-objective　optimization　problems(MaOPs)　on　account　of　the　loss　of　the　selection　pressure　of　the　non-dominant　candidate　solutions　toward　the　Pareto　front　and　the　ineffective　design　of　the　diversity　maintenance　mechanism.　This　paper　proposes　a　many-objective　evolutionary　algorithm　based　on　vector　guidance.　In　this　algorithm,　the　value　of　vector　angle　distance　scaling(VADS)　is　applied　to　balance　convergence　and　diversity　in　environmental　selection.　In　addition,　tournament　selection　based　on　the　aggregate　fitness　value　of　VADS　is　applied　to　generate　a　high　quality　offspring　population.　Besides,　we　adopt　an　adaptive　strategy　to　adjust　the　reference　vector　dynamically　according　to　the　scales　of　the　objective　functions.　Finally,　the　performance　of　the　proposed　algorithm　is　compared　with　five　state-of-the-art　many-objective　evolutionary　algorithms　on　52　instances　of　13　MaOPs　with　diverse　characteristics.　Experimental　results　show　that　the　proposed　algorithm　performs　competitively　when　dealing　many-objective　with　different　types　of　Pareto　front.　©　2021　-　IOS　Press.　All　rights　reserved.