Dynamic　multi-objective　optimization　problems　(DMOPs)　are　multi-objective　problems　that　are　influenced　by　dynamically　changing　environmental　parameters.　Most　current　algorithms　for　solving　DMOPs　only　respond　to　dynamic　changes　in　the　decision　space　or　objective　space　and　also　ignore　the　impact　of　the　type　of　DMOPs　on　the　algorithm.　The　changes　in　the　Pareto-optimal　solution　(POS)　and　Pareto-optimal　front　(POF)　may　affect　the　type　of　change　in　DMOPs.　Therefore,　this　paper　proposed　an　adaptive　dynamic　multi-objective　evolutionary　algorithm　for　type　detection　(TDA-DMOEA).　First,　the　dynamic　detection　operator　is　designed　to　identify　the　types　of　dynamic　problems.　The　Wilcoxon　signed-rank　test　and　Hyper　Volume　(HV)　are　used　to　detect　the　difference　of　POS　and　POF　in　two　adjacent　environments　respectively.　Then,　different　response　strategies　are　designed　to　cope　with　different　types　of　changes　in　DMOP.　In　particular,　a　multi-angle-based　transfer　learning　method　(MA-TL)　with　a　closed　kernel　function　is　derived　when　faced　with　simultaneous　changes　in　POS　and　POF.　Finally,　a　comprehensive　study　of　the　commonly　used　benchmark　set　of　DMOPs　is　presented,　and　the　proposed　algorithm　achieves　better　performance　in　optimizing　DMOPs.　©　2023　Elsevier　Inc.