Particle　swarm　optimization　(PSO)　is　one　of　the　artificial　intelligence　algorithms　and　has　been　proved　to　be　an　effective　global　optimization　method.　In　this　paper,　a　novel　particle　swarm　optimization　algorithm　(DC-PSO)　combining　the　discussion　mechanism　and　chaos　strategy　is　proposed　to　improve　the　standard　PSO　(SPSO)　which　is　easy　to　trap　into　local　optimum　and　has　the　problems　of　premature　convergence.　The　four　main　strategies　adapted　in　DC-PSO　are　chaos　initializes　the　population,　dynamic　change　of　population,　discussion　mechanism　of　double　populations　and　random　variation　of　particles.　Experiments　are　performed　on　eight　benchmark　functions　to　test　the　accuracy　and　performance　of　DC-PSO.　Experiments　show　the　proposed　DC-PSO　performs　better　than　DEs　intelligent　algorithms.　©　2019　IEEE.