Centralized　particle　swarm　optimization　(PSO)　does　not　fully　exploit　the　potential　of　distributed　or　parallel　computing　and　suffers　from　single-point-of-failure.　Particularly,　each　particle　in　PSO　comprises　a　potential　solution　(e.g.,　traveling　route　and　neural　network　model　parameters)　which　is　essentially　viewed　as　private　data.　Unfortunately,　previously　neither　centralized　nor　distributed　PSO　algorithms　fail　to　protect　privacy　effectively.　Inspired　by　secure　multiparty　computation　and　multiagent　system,　this　article　proposes　a　privacy-preserving　multiagent　PSO　algorithm　(called　PriMPSO)　to　protect　each　particle＇s　data　and　enable　private　data　sharing　in　a　privacy-preserving　manner.　The　goal　of　PriMPSO　is　to　protect　each　particle＇s　data　in　a　distributed　computing　paradigm　via　existing　PSO　algorithms　with　competitive　performance.　Specifically,　each　particle　is　executed　by　an　independent　agent　with　its　own　data,　and　all　agents　jointly　perform　global　optimization　without　sacrificing　any　particle＇s　data.　Thorough　investigations　show　that　selecting　an　exemplar　from　all　particles　and　updating　particles　through　the　exemplar　are　critical　operations　for　PSO　algorithms.　To　this　end,　this　article　designs　a　privacy-preserving　exemplar　selection　algorithm　and　a　privacy-preserving　triple　computation　protocol　to　select　exemplars　and　update　particles,　respectively.　Strict　privacy　analyses　and　extensive　experiments　on　a　benchmark　and　a　realistic　task　confirm　that　PriMPSO　not　only　protects　particles＇　privacy　but　also　has　uniform　convergence　performance　with　the　existing　PSO　algorithm　in　approximating　an　optimal　solution.