Flood Control Optimization of Reservoir Group Based on Improved Sparrow Algorithm (ISSA)

The optimal control problem of reservoir group flood is a complex, nonlinear, high-dimensional, multi-peak extremum with many complex constraints and interdependent decision variables. traditional algorithm slow easily falls into the local optimum when solving optimization groups. intelligent has characteristics fast computing speed strong searching ability, which can make up for shortcomings algorithm. In this study, improved sparrow (ISSA) combining Cauchy mutation reverse learning strategy used to solve This study takes Sanmenxia Reservoir Xiaolangdi on mainstream Yellow River as research object Huayuankou downstream point establish joint operation model cascade reservoirs. results (ISSA), particle swarm (POS) (SSA) are compared analyzed. show that ISSA problem, maximum peak flow garden entrance 11,676.3 m3, cutting rate 48%. effect obviously better than other two algorithms. provides new effective way