基于EDA与GA算法的水库群短期防洪优化调度研究
Optimal Operation Research of Reservoirs Short-Time Flood Control Based on EDA and GA Algorithms

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Keywords: 水库群优化调度，马斯京根，分布估计算法，遗传算法
Reservoirs Optimal Operation, Muskingum, Estimation of Distribution Algorithm, Genetic Algorithm

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Abstract:

针对水库群短期防洪调度，建立了以下游防洪控制站点最大洪峰流量最小为目标、加入马斯京根演算作为约束条件的水库群防洪优化调度模型。由于马斯京根河道演算法的滞时，加重了维数灾难题，探讨采用分布估计算法求解模型。以西江流域龙滩、百色水库群优化调度为例，分别采用分布估计算法、遗传算法进行模型求解，试验表明：分布估计算法比遗传算法收敛速度更快，并能够得到较好削减洪峰效果。
Since the Muskingum flood routing is used in reservoir operation for short-time flood control, the curse of dimensionality is aggravated due to the time-lags of food routing. In order to solve this problem, a re-servoirs optimal flood control operation model is established to minimize the flood peak of the down-stream flood control station with the formula of Muskingum as the additional constraints. The Longtan and Bose reservoirs in the Xijiang Basin are used as a case study. The Estimation of Distribution Algorithm (EDA) is proposed to solve the optimal model, and the results are compared with that of the Genetic Algorithm (GA). The experiment demonstrates that the EDA outperforms the GA in terms of the convergence rate and the flood peak reduction.

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