A　discrete　particle　swarm　optimization　(DPSO)　algorithm　is　developed.　To　obtain　a　better　approximation　of　true　Pareto　front,　the　phenotype　sharing　function　of　the　objective　space　is　applied　in　the　fitness　function.　Inspired　by　the　physics　of　genetic　algorithm　(GA),　the　principles　of　mutation　and　crossover　operator　in　GA　are　incorporated　into　the　proposed　PSO　algorithm　to　achieve　better　diversity　and　break　away　from　local　optima.　The　global　convergence　of　the　proposed　algorithm　is　proved　by　the　theorem　of　Markov　chain.　The　experimental　results　show　that　DPSO　is　efficient　and　has　good　performance　to　problems　with　increased　size.