The　selection　of　global　best　(Gbest)　exerts　a　high　influence　on　the　searching　performance　of　multi-objective　particle　swarm　optimization　algorithm　(MOPSO).　The　candidates　of　MOPSO　in　external　archive　are　always　estimated　to　select　Gbest.　However,　in　most　estimation　methods,　the　candidates　are　considered　as　the　Gbest　in　a　fixed　way,　which　is　difficult　to　adapt　to　varying　evolutionary　requirements　for　balance　between　convergence　and　diversity　of　MOPSO.　To　deal　with　this　problem,　an　adaptive　candidate　estimation-assisted　MOPSO　(ACE-MOPSO)　is　proposed　in　this　paper.　First,　the　evolutionary　state　information,　including　both　the　global　dominance　information　and　global　distribution　information　of　non-dominated　solutions,　is　introduced　to　describe　the　evolutionary　states　to　extract　the　evolutionary　requirements.　Second,　an　adaptive　candidate　estimation　method,　based　on　two　evaluation　distances,　is　developed　to　select　the　excellent　leader　for　balancing　convergence　and　diversity　during　the　dynamic　evolutionary　process.　Third,　a　leader　mutation　strategy,　using　the　elite　local　search　(ELS),　is　devised　to　select　Gbest　to　improve　the　searching　ability　of　ACE-MOPSO.　Fourth,　the　convergence　analysis　is　given　to　prove　the　theoretical　validity　of　ACE-MOPSO.　Finally,　this　proposed　algorithm　is　compared　with　popular　algorithms　on　twenty-four　benchmark　functions.　The　results　demonstrate　that　ACE-MOPSO　has　advanced　performance　in　both　convergence　and　diversity.