With　the　increase　of　the　number　of　objectives　in　multi-objective　optimization　problems,　the　proportion　of　non-dominated　individuals　in　the　population　will　increase　exponentially,　and　the　multi-objective　evolutionary　algorithm　based　on　the　traditional　Pareto　dominance　relation　lacks　sufficient　selection　pressure,　which　degenerates　the　convergence　of　the　algorithm.　In　addition,　the　effectiveness　of　existing　density　estimation　approaches　to　distinguish　the　individuals　in　high-dimensional　space　decreases,　resulting　in　poor　performance　in　maintaining　diversity.　In　view　of　the　above　problems,　this　paper　proposes　a　many-objective　evolutionary　algorithm　based　on　improved　dominance　relation.　First,　an　improved　dominance　relation　is　proposed,　the　individual　with　the　best　convergence　is　kept　in　each　niche,　which　increases　the　selection　pressure.　Second,　a　dynamic　fitness　function　is　proposed　so　that　individuals　with　better　convergence　and　diversity　can　be　retained　adaptively.　Third,　an　adaptive　student　distribution　crossover　operator　is　proposed,　which　further　improves　the　convergence　and　diversity　of　our　algorithm.　The　experimental　results　show　that　the　population　obtained　by　MaOEA-IDR　is　better　than　the　existing　algorithms.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.