The　huge　objective　space　makes　some　representative　multiobjective　optimization　algorithms　face　challenges.　A　many-objective　particle　swarm　optimization　based　on　adaptive　fuzzy　dominance　(MAPSOAF)　is　proposed.　An　adaptive　fuzzy　dominance　relation　is　defined　to　adjust　the　threshold　of　fuzzy　dominance　adaptively　to　improve　the　convergence　of　MAPSOAF.　Second,　a　perturbation　term　is　added　to　the　particle＇s　velocity　formula　by　selecting　an　elite　member　from　the　external　archive　to　conquer　premature　convergence　and　to　enhance　the　diversity　of　population.　In　addition,　a　method　of　simplified　Harmonic　normalized　distance　is　utilized　to　evaluate　the　density　of　individuals　and　reduce　the　computational　cost　when　while　improving　the　population　diversity.　MAPSOAF　is　compared　with　other　five　high-performance　multiobjective　evolutionary　algorithms　on　the　benchmark　test　set　DTLZ　{1,　2,　4,　5},　and　the　results　show　that　the　proposed　MAPSOAF　has　more　significant　performance　advantages　in　convergence　and　diversity　over　the　peering　algorithms.　Copyright　©　2018　Acta　Automatica　Sinica.　All　rights　reserved.