A　multiobjective　evolutionary　algorithm　based　on　decomposition　(MOEA/D)　decomposes　a　multiobjective　optimization　problem　(MOP)　into　a　number　of　scalar　optimization　subproblems　and　optimizes　them　in　a　collaborative　manner.　In　MOEA/D,　decomposition　mechanisms　are　used　to　push　the　population　to　approach　the　Pareto　optimal　front　(POF),　while　a　set　of　uniformly　distributed　weight　vectors　are　applied　to　maintain　the　diversity　of　the　population.　Penalty-based　boundary　intersection　(PBI)　is　one　of　the　approaches　used　frequently　in　decomposition.　In　PBI,　the　penalty　factor　plays　a　crucial　role　in　balancing　convergence　and　diversity.　However,　the　traditional　PBI　approach　adopts　a　fixed　penalty　value,　which　will　significantly　degrade　the　performance　of　MOEA/D　on　some　MOPs　with　complicated　POFs.　This　paper　proposes　an　angle-based　adaptive　penalty　(AAP)　scheme　for　MOEA/D,　called　MOEA/D-AAP,　which　can　dynamically　adjust　the　penalty　value　for　each　weight　vector　during　the　evolutionary　process.　Six　newly　designed　benchmark　MOPs　and　an　MOP　in　the　wastewater　treatment　process　are　used　to　test　the　effectiveness　of　the　proposed　MOEA/D-AAP.　Comparison　experiments　demonstrate　that　the　AAP　scheme　can　significantly　improve　the　performance　of　MOEA/D.　(C)　2018　Elsevier　B.V.　All　rights　reserved.