The　selection　of　global　best　(gBest)　is　an　important　and　challenging　issue　for　multiobjective　particle　swarm　optimization　(MOPSO)　algorithms.　In　this　paper,　a　distribution-knowledge-guided　assessment　strategy　(KS)　is　proposed　to　obtain　the　suitable　gBest　in　MOPSO.　The　novelties　of　KS-MOPSO　include　the　following　three　aspects.　First,　the　distribution　knowledge,　including　both　the　current　and　historical　distributions　of　nondominated　solutions,　is　designed　to　describe　the　distribution　information　of　the　optimal　solutions.　Second,　an　adaptive　assessment　mechanism　using　this　knowledge　is　designed　to　select　the　appropriate　gBest　to　improve　the　search　performance.　Third,　an　optimal　technique　is　developed　to　update　the　archive　to　improve　the　computational　efficiency.　Finally,　the　performance　of　KS-MOPSO　is　compared　with　that　of　other　algorithms　on　benchmark　functions　and　a　zinc　electrolysis　optimization　problem.　The　experimental　results　show　significant　improvement　over　these　state-of-the-art　algorithms.