In　view　of　the　deadline-constrained　scientific　workflow　scheduling　on　multi-cloud,　an　adaptive　discrete　particle　swarm　optimization　with　genetic　algorithm　(ADPSOGA)　was　proposed,　which　aimed　to　minimize　the　execution　cost　of　workflow　while　meeting　its　deadline　constrains.　Firstly,　the　data　transfer　cost,　the　shutdown　and　boot　time　of　virtual　machines,　and　the　bandwidth　fluctuations　among　different　cloud　providers　were　considered　by　this　method.　Secondly,　in　order　to　avoid　the　premature　convergence　of　traditional　particle　swarm　optimization　(PSO),　the　randomly　two-point　crossover　operator　and　randomly　one-point　mutation　operator　of　the　genetic　algorithm　(GA)　was　introduced.　It　could　effectively　improve　the　diversity　of　the　population　in　the　process　of　evolution.　Finally,　a　cost-driven　strategy　for　the　deadline-constrained　workflow　was　designed.　It　both　considered　the　data　transfer　cost　and　the　computing　cost.　Experimental　results　show　that　the　ADPSOGA　has　better　performance　in　terms　of　deadline　and　cost　reducing　in　the　fluctuant　environment.　©　2018,　Editorial　Board　of　Journal　on　Communications.　All　right　reserved.