School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou, Henan 450046, China.
Yellow River Institute of Hydraulic Research, Zhengzhou, Henan 450003, China.
Comput Intell Neurosci. 2022 May 10;2022:4123421. doi: 10.1155/2022/4123421. eCollection 2022.
With the rapid development of China's social economy, it is the most important task for the water conservancy industry to make use of the existing water conservancy engineering measures to carry out the research on river basin flood control dispatching. Large-scale joint operation of river basins usually needs to consider meteorological and hydrological conditions, historical flood data, multireservoir engineering conditions, and multiple flood control targets, which is a complex decision-making problem. Therefore, electing the optimal operation model of reservoir flood control optimization is very important. In this paper, Luanhe River Basin is taken as the research area, and three kinds of constraints, namely, water balance constraint, reservoir flood control capacity constraint, and water release decision constraint, are set to construct the flood control optimization model. Taking the minimum square of the sum of reservoir discharge and interval flood discharge as the objective function, genetic algorithm (GA), particle swarm optimization (PSO), Spider swarm optimization (SSO), and grey wolf optimization (GWO) are introduced into flood control optimal operation to seek the minimum value of objective function, and the results are compared and analyzed. Through the analysis of optimization results, the optimization ability and convergence effect of grey wolf optimization algorithm are better than those of genetic algorithm and particle algorithm, and the results are more stable than those of spider swarm algorithm. It has a good model structure and can make full use of the results of three wolf groups for optimization. Through the analysis of scheduling results, the results of genetic algorithm and particle swarm optimization algorithm are similar, while those of spider swarm optimization algorithm and grey wolf optimization algorithm are similar and slightly better than those of the first two. Moreover, the search range of grey wolf optimization algorithm for solving long sequence problems is wider and the calculation time is shorter. Therefore, the grey wolf optimization algorithm can be applied to solve the flood control operation optimization model of Panjiakou Reservoir Group.
随着中国社会经济的快速发展，利用现有水利工程措施开展流域防洪调度研究是水利行业的重中之重。大规模流域联合运行通常需要考虑气象水文条件、历史洪水数据、多水库工程条件和多个防洪目标，这是一个复杂的决策问题。因此，选择最优的水库防洪优化运行模型非常重要。本文以滦河流域为研究区域，设置水量平衡约束、水库防洪能力约束和放水决策约束 3 种约束条件，构建防洪优化模型。以水库泄量和区间洪量之和的平方和最小为目标函数，引入遗传算法（GA）、粒子群优化算法（PSO）、蜘蛛群优化算法（SSO）和灰狼优化算法（GWO）进行防洪优化调度，寻求目标函数的最小值，并对结果进行对比分析。通过优化结果分析，灰狼优化算法的优化能力和收敛效果优于遗传算法和粒子群算法，结果比蜘蛛群算法更稳定。它具有良好的模型结构，可以充分利用三狼群的优化结果。通过调度结果分析，遗传算法和粒子群优化算法的结果相似，而蜘蛛群优化算法和灰狼优化算法的结果相似，略优于前两种。此外，灰狼优化算法在解决长序列问题时的搜索范围更广，计算时间更短。因此，灰狼优化算法可应用于潘家口水库群防洪调度优化模型的求解。
