In　cloud　computing　environments,　data　centers　can　provide　high-performance　computing　resources　and　distributed　storage　space.　Scientific　workflows　often　need　to　be　implemented　across　multiple　data　centers,　where　copious　amounts　of　application　data　are　stored.　Moving　data　across　geographically　distributed　data　centers　leads　to　intolerable　delays　and　hinders　the　efficient　execution　of　scientific　workflows,　which　are　large-scale　data-intensive.　Reasonable　data　placement　can　reduce　data　scheduling　between　the　data　centers　effectively.　In　this　paper,　an　adaptive　discrete　particle　swarm　optimization　(PSO)　algorithm　based　on　genetic　algorithm　has　been　proposed　to　decrease　the　number　of　data　transmissions　across　data　centers.　The　algorithm　overcame　the　premature　convergence　defect　of　PSO　by　introducing　the　mutation　and　crossover　of　genetic　algorithm.　Moreover,　it　effectively　improved　the　diversity　in　the　process　of　population　evolution.　Compared　with　the　previous　work,　the　simulation　results　showed　that　the　proposed　strategy　greatly　reduced　the　volume　of　data　transfer　while　reducing　the　number　of　data　movement　across　data　centers.　©　2018　IEEE.