In　general,　for　the　iteration　process　of　an　evolutionary　algorithm　(EA),　there　exists　the　problem　of　uneven　distribution　of　individuals　in　the　target　space　for　both　multi-objective　and　single-objective　optimization　problems.　This　uneven　distribution　significantly　degrades　the　population　diversity　and　convergence　speed.　This　paper　proposes　an　adaptive　hybrid　evolutionary　immune　algorithm　based　on　a　uniform　distribution　selection　mechanism　(AU-DHEIA)　for　solving　MOPs　efficiently.　In　AUDHEIA,　the　individuals　in　the　population　are　mapped　to　a　hyperplane,　which　is　correlated　with　the　objective　space　and　are　clustered　to　increase　the　diversity　of　solutions.　To　improve　the　distribution　of　the　solutions,　the　mapped　hyperplane　is　evenly　sectioned.　With　the　constantly　changing　distribution　during　the　iteration,　a　threshold　as　a　standard　for　judging　the　distribution　level　is　adjusted　adaptively.　When　the　threshold　is　not　satisfied　in　the　corresponding　interval,　the　distribution　enhancement　module　is　activated.　Then,　the　same　number　of　individuals　should　be　selected　in　each　interval.　However,　sometimes,　there　are　insufficient　or　no　individuals　in　the　interval　during　the　iterative　process.　To　obtain　sufficient　individuals,　the　limit　optimization　variation　strategy　of　the　best　individual　is　adopted.　Experiments　show　that　this　algorithm　can　escape　from　local　optima　and　has　a　high　convergence　speed.　Moreover,　the　distribution　and　convergence　of　this　algorithm　are　superior　to　the　peer　algorithms　tested　in　this　paper.　(C)　2019　Published　by　Elsevier　Inc.