Compared　to　traditional　distributed　computing　environments　such　as　grids,　cloud　computing　provides　a　more　cost-effective　way　to　deploy　scientific　workflows.　Each　task　of　a　scientific　workflow　requires　several　large　datasets　that　are　located　in　different　datacenters,　resulting　in　serious　data　transmission　delays.　Edge　computing　reduces　the　data　transmission　delays　and　supports　the　fixed　storing　manner　for　scientific　workflow　private　datasets,　but　there　is　a　bottleneck　in　its　storage　capacity.　It　is　a　challenge　to　combine　the　advantages　of　both　edge　computing　and　cloud　computing　to　rationalize　the　data　placement　of　scientific　workflow,　and　optimize　the　data　transmission　time　across　different　datacenters.　In　this　study,　a　self-adaptive　discrete　particle　swarm　optimization　algorithm　with　genetic　algorithm　operators　(GA-DPSO)　was　proposed　to　optimize　the　data　transmission　time　when　placing　data　for　a　scientific　workflow.　This　approach　considered　the　characteristics　of　data　placement　combining　edge　computing　and　cloud　computing.　In　addition,　it　considered　the　factors　impacting　transmission　delay,　such　as　the　bandwidth　between　datacenters,　the　number　of　edge　datacenters,　and　the　storage　capacity　of　edge　datacenters.　The　crossover　and　mutation　operators　of　the　genetic　algorithm　were　adopted　to　avoid　the　premature　convergence　of　traditional　particle　swarm　optimization　algorithm,　which　enhanced　the　diversity　of　population　evolution　and　effectively　reduced　the　data　transmission　time.　The　experimental　results　show　that　the　data　placement　strategy　based　on　GA-DPSO　can　effectively　reduce　the　data　transmission　time　during　workflow　execution　combining　edge　computing　and　cloud　computing.　©　2005-2012　IEEE.