To　improve　the　diversity　and　convergence　of　optimal　solutions　in　multiobjective　particle　swarm　optimization　(MOPSO)　algorithm,　a　multiobjective　particle　swarm　optimization　algorithm,　based　on　the　diversity　information　and　convergence　degree,　named　dicdMOPSO,　is　developed　in　this　paper.Firstly,　a　global　optimal　solution　selection　mechanism,　based　on　the　distribution　of　optimal　solutions　in　the　knowledge　base　with　the　diversity　information　of　non-dominated　solutions,　is　introduced　to　balance　the　evolutionary　process　of　population　to　improve　the　diversity　and　convergence　of　non-dominated　solutions.Then,　to　enhance　global　exploration　and　local　exploitation　abilities　of　particles,　a　flight　parameter　adjustment　mechanism　is　proposed　to　obtain　the　particles　with　better　diversity　and　convergence　by　using　the　population　diversity　information.Finally,　the　experiment　results　demonstrate　that,　compared　with　other　multiobjective　algorithms,　this　proposed　dicdMOPSO　algorithm　can　not　only　obtain　the　optimal　solutions　with　better　diversity,　but　also　be　faster　to　catch　the　Pareto　front.　©　2018,　Chinese　Institute　of　Electronics.　All　right　reserved.