The　decomposition-based　evolutionary　algorithm　(MOEA/D)　has　attained　excellent　performance　in　solving　optimization　problems　involving　multiple　conflicting　objectives.　However,　the　Pareto-optimal　front　(POF)　of　many　multiobjective　optimization　problems　(MOPs)　has　irregular　properties,　which　weakens　the　performance　of　MOEA/D.　To　address　this　issue,　we　devise　a　dynamic　transfer　reference　point-oriented　MOEA/D　with　local　objective-space　knowledge　(DTR-MOEA/D).　The　design　principle　is　based　on　three　original　and　rigorous　mechanisms.　First,　the　individuals　are　projected　onto　a　line　segment　(two-objective　case)　or　a　3-D　plane　(three-objective　case)　after　being　normalized　in　the　objective　space.　The　line　segment　or　the　plane　is　divided　into　three　different　regions:　1)　the　central　region;　2)　the　middle　region;　and　3)　the　edge　region.　Second,　a　dynamic　transfer　criterion　of　the　reference　point　is　developed　based　on　the　population　density　relationships　in　different　regions.　Third,　a　strategy　of　population　diversity　enhancement　guided　by　local　objective-space　knowledge　is　adopted　to　improve　the　diversity　of　the　population.　Finally,　the　experimental　results　conducted　on　16　benchmark　MOPs　and　eight　modified　MOPs　with　irregular　POF　shapes　verify　that　the　proposed　DTR-MOEA/D　has　attained　a　strong　competitiveness　compared　with　other　representative　algorithms.