Cloud　computing　provides　a　cost-effective　deploying　environment　for　hosting　and　executing　workflows　as　its　elasticity,　scalability　and　pay-per-use　model.　Scientific　applications　are　normally　compute-　or　resource-intensive,　and　how　to　run　them　in　the　cloud　while　both　meeting　QoS　of　users　and　guaranteeing　the　benefits　of　cloud　service　providers　(CSPs)　is　still　a　challenge　and　depends　mainly　on　workflow　scheduling.　In　this　article,　we　propose　a　hybrid　optimization　approach,　PSO+LOA,　i.e.,　a　combination　of　particle　swarm　optimization　(PSO)　and　lion　optimization　algorithm　(LOA)　for　scheduling　workflows　in　the　cloud　to　minimize　the　total　execution　time　under　budget　constraints.　The　main　contributions　of　our　work　are:　(1)　A　Euclidean　distance　(ED)　aware　particle　reposition　strategy　is　defined　for　two　close　particles,　so　as　to　separate　them　away　from　each　other,　hence　enhancing　the　capability　of　escaping　from　local　optima.　(2)　To　improve　the　search　and　convergence　efficiency　of　original　PSO,　we　modify　the　velocity　update　equation　by　introducing　adaptive　parameters.　(3)　Inspired　by　the　multiple-swarm　co-evolutionary　mechanism　of　LOA,　we　integrate　PSO　with　LOA　to　make　a　good　balance　between　exploration　and　exploitation　during　the　whole　optimization　process.　Extensive　experiments　are　conducted　over　well-known　scientific　workflows　with　different　sizes　and　types　through　WorkflowSim.　The　experimental　results　demonstrate　that　in　most　cases,　PSO+LOA　outperforms　the　existing　algorithms　in　the　extent　of　budget　constraint　satisfiability,　solution　quality,　i.e.,　it　can　generate　much　better　solutions　which　meet　the　needs　of　different　budget　constraints,　especially　for　large-scale　applications,　such　as　the　average　relative　deviation　index　for　PSO+LOA　and　genetic　algorithm　(GA)　are　0.03%　and　0.20%,　respectively.