For the short-term scheduling of cascaded hydropower stations

this paper constructs a network coordination short-term plant scheduling model to consider both peak shaving effect and power generation benefit

along with the factors of their interactive constraints. This new model is based on the water balance equation and takes the factors of unit output limitations

irregular vibration zones

and the backwater effect of tailwater as model constraints. Then

we develop an improved locust visual evolutionary neural network with adaptive parameter tuning to solve the model

through modifying the previous one by incorporating the visual residual mechanism

the half wave rectification mechanism

and local mean filtering. Further

a matrix-based state updating strategy is worked out to achieve the transition of states

resorting to the output of the visual neural network

the position updating strategy of the mountain gazelle optimization algorithm

and a chaotic mapping. Comparative numerical experiments have verified our new neural network has significant advantages in application: at least seven of thirteen benchmark examples

and at least two of three application scenes at the Beipanjiang cascade hydropower station in Guizhou.