Recently,　the　development　of　evolutionary　multi-objective　optimization　(EMO)　algorithm　to　deal　with　multi-objective　optimization　problems　(with　four　or　more　objectives)　has　gradually　become　a　hot　spot.　NSGA-III　algorithm　is　effective　in　dealing　with　evolutionary　multi-objective　optimization　problems.　In　this　paper,　we　recognize　some　advantages　of　the　existing　NSGA-III　algorithm　and　make　some　improvements.　The　improved　NSGA-III　algorithm　has　higher　adaptability　and　can　provide　more　dense　Pareto-optimal　front　under　the　same　amount　of　computation.　The　improved　NSGA-III　algorithm　is　applied　to　many　multi-objective　testing　problems　with　3　to　8　objectives,　and　its　performance　is　compared　with　the　existing　multi-objective　evolutionary　algorithms.　Experimental　results　show　that　the　improved　algorithm　can　produce　satisfactory　results　for　all　the　problems　considered　in　this　study.　Among　the　28　environments　with　all　values,　the　improved　NSGA-III　algorithm　has　22　optimal　values,　accounting　for　78.57%.　After　that,　we　analyze　the　results　and　put　forward　the　future　improvement　and　research　direction.　©　2022,　Springer　Nature　Singapore　Pte　Ltd.