Most　multi-objective　particle　swarm　optimization　algorithms,　which　have　demonstrated　their　good　performance　on　various　practical　problems　involving　two　or　three　objectives,　face　significant　challenges　in　complex　problems.　For　overcoming　this　challenges,　a　multi-objective　particle　swarm　optimization　algorithm　based　on　enhanced　selection(ESMOPSO)　is　proposed.　In　order　to　increase　the　ability　of　exploration　and　exploitation,　enhanced　selection　strategy　is　designed　to　update　personal　optimal　particles,　and　objective　function　weighting　is　used　to　update　global　optimal　particle　adaptively.　In　addition,　R2　indicator　is　incorporated　into　the　achievement　scalarizing　function　to　layer　particles　in　archive,　which　promotes　the　archive　update.　Besides,　Gaussian　mutation　strategy　is　designed　to　avoid　particles　falling　into　local　optimum,　and　polynomial　mutation　is　applied　in　archive　to　increase　the　diversity　of　elite　solutions.　The　performance　of　the　proposed　algorithm　is　validated　and　compared　with　some　state-of-the-art　algorithms　on　a　number　of　test　problems.　Experimental　results　demonstrate　that　ESMOPSO　algorithm　shows　very　competitive　performance　when　dealing　with　complex　MOPs.