Resumen
The proper dispatching of hydraulic structures in water diversion projects is a desirable way to maximize project benefits. This study aims to provide a reliable, optimal scheduling model for hydraulic engineering to improve the regional water environment. We proposed an improved gravitational search algorithm (IPSOGSA) based on multi-strategy hybrid technology to solve this practical problem. The opposition-based learning strategy, elite mutation strategy, local search strategy, and co-evolution strategies were employed to balance the exploration and exploitation of the algorithm through the adaptive evolution of the elite group. Compared with several other algorithms, the preponderance of the proposed algorithm in single-objective optimization problems was demonstrated. We combined the water quality mechanism model, an artificial neural network (ANN), and the proposed algorithm to establish the optimal scheduling model for hydraulic structures. The backpropagation neural network (IGSA-BPNN) trained by the improved algorithm has a high accuracy, with a coefficient of determination (R2) over 0.95. Compared to the two traditional algorithms, the IGSA-BPNN model was, respectively, improved by 1.5% and 0.9% on R2 in the train dataset, and 1.1% and 1.5% in the test dataset. The optimal scheduling model for hydraulic structures led to a reduction of 46~69% in total power consumption while achieving the water quality objectives. With the lowest cost scheme in practice, the proposed intelligent scheduling model is recommended for water diversion projects in plain river networks.