It　is　a　major　challenge　to　combine　the　advantages　of　both　public　and　private　clouds　to　rationalize　the　data　placement　of　scientific　workflow　with　private　data,　and　optimize　the　transmission　time　of　large-scale　data　across　different　data　centers.　By　considering　the　characteristics　of　data　placement　in　hybrid　cloud　and　combining　the　dependencies　among　scientific　workflow,　an　adaptive　Discrete　Particle　Swarm　Optimization　Algorithm　based　on　Genetic　Algorithm　operators　(GA-DPSO)　was　proposed,　which　considered　the　influence　on　the　transmission　time　such　as　the　bandwidth　between　data　centers,　the　number　and　the　capacity　of　private　cloud　data　centers.　Through　introducing　the　crossover　operator　and　the　mutation　operator　of　the　genetic　algorithm,　the　premature　convergence　problem　of　the　particle　swarm　optimization　was　avoided,　which　enhanced　the　diversity　of　population　evolution　and　effectively　compressed　data　transmission　time.　The　experimental　results　showed　that　the　data　placement　strategy　based　on　GA-DPSO　could　effectively　reduce　the　data　transmission　time　of　scientific　workflow　in　hybrid　cloud.　©　2019,　Editorial　Department　of　CIMS.　All　right　reserved.