Decomposition-based　multi-objective　optimization　algorithms　have　been　shown　to　be　successful　in　solving　multi-objective　optimization　problems　(MOPs).　Since　the　shape　is　unknown　before　the　search,　the　classical　decomposition　method　relies　on　a　set　of　uniformly　distributed　reference　vectors　to　divide　the　objective　space　into　multiple　subregions.　However,　the　the　distribution　of　reference　vectors　produced　by　the　Das　and　Dennis　systematic　approach　is　difficult　to　maintain　the　diversity　of　(extremely)　concave　and　(extremely)　convex　problems.　To　remedy　this　issue,　a　multi-objective　evolutionary　algorithm　based　on　cone　heart　point　adjustment　vectors　is　proposed　to　solve　concave　and　convex　problems　(CHP-MOEA).　In　this　paper,　the　concavity　or　convexity　is　first　according　to　the　intermediate　objective　vector.　Then,　the　distribution　of　reference　vectors　are　adjusted　according　to　the　cone　heart　point　on　the　basis　of　the　traditional　vectors.　Finally,　experimental　results　on　31　benchmark　problems　show　that　the　proposed　algorithm　exhibits　better　performance　than　other　state-of-the-art　algorithms.　©　2022　IEEE.