To　seek　the　optimal　value　for　hydrological　frequency　parameters,　and　then　to　obtain　a　higher　precision　of　hydrological　characteristics　value,　an　optimization　algorithm　of　hydrologic　frequency　parameter　based　on　particle　swarm　optimization　(PSO)　and　adaptive　genetic　algorithm　(AGA)　was　proposed.　Based　on　the　rule　of　minimum　sum　of　squared　residuals,　the　rule　of　the　least　sum　of　deviation　absolute　value　and　the　rule　of　relative　deviation　minimum　sum　of　squared　residuals,　the　algorithm　was　constructed　which　was　applied　to　hydrological　frequency　parameter　optimization　model.　Adaptive　genetic　operators　in　particle　swarm　optimization　algorithm　was　introduced,　by　combining　the　global　search　ability　of　genetic　algorithm　with　quicker　convergence　rate　of　particle　swarm　algorithm　effectively,　and　adaptively,　and　the　crossover　and　mutation　probability　was　improved,　thereby　a　set　of　adaptive　hybrid　algorithm　was　formed,　the　optimum　parameters　of　the　hydrologic　frequency　was　obtained　through　the　model.　By　using　a　municipal　meteorological　center　of　rainfall　data　as　an　example,　the　algorithm　was　compared　with　other　conventional　methods　in　this　paper.　Results　show　that　the　fitting　precision　and　fitness　effect　of　the　parameter　estimation　using　the　algorithm　are　superior　to　conventional　methods,　and　the　algorithm　provides　reference　for　hydrologic　frequency　analysis　field.　©　2016,　Beijing　University　of　Technology.　All　right　reserved.