Multiobjective　particle　swarm　optimization　(MOPSO)　algorithms　have　attracted　much　attention　for　their　promising　performance　in　solving　multiobjective　optimization　problems　(MOPs).　In　this　paper,　an　adaptive　MOPSO　(AMOPSO)　algorithm,　based　on　a　hybrid　framework　of　the　solution　distribution　entropy　and　population　spacing　(SP)　information,　is　developed　to　improve　the　search　performance　in　terms　of　convergent　speed　and　precision.　First,　an　adaptive　global　best　(gBest)　selection　mechanism,　based　on　the　solution　distribution　entropy,　is　introduced　to　analyze　the　evolutionary　tendency　and　balance　the　diversity　and　convergence　of　nondominated　solutions　in　the　archive.　Second,　an　adaptive　flight　parameter　adjustment　mechanism,　using　the　population　SP　information,　is　proposed　to　obtain　the　distribution　of　particles　with　suitable　diversity　and　convergence,　which　can　balance　the　global　exploration　and　local　exploitation　abilities　of　the　particles.　Third,　based　on　the　gBest　selection　mechanism　and　the　adaptive　flight　parameter　mechanism,　this　proposed　AMOPSO　algorithm　not　only　has　high　accuracy,　but　also　attain　a　set　of　optimal　solutions　with　better　diversity.　Finally,　the　performance　of　the　proposed　AMOPSO　algorithm　is　validated　and　compared　with　other　five　state-of-the-art　algorithms　on　a　number　of　benchmark　problems　and　water　distribution　system.　The　experimental　results　validate　the　effectiveness　of　the　proposed　AMOPSO　algorithm,　as　well　as　demonstrate　that　AMOPSO　outperforms　other　MOPSO　algorithms　in　solving　MOPs.