人民珠江2024，Vol.45Issue(2)：60-68,9.DOI:10.3969/j.issn.1001-9235.2024.02.008
哈里斯鹰算法在广义非线性马斯京根参数优化中的应用
Application of Harris Hawks Optimization Algorithm in Optimization of Generalized Nonlinear Muskingum Parameters
摘要
Abstract
The Muskingum model plays an important role in river flood simulation,and its simulation accuracy relies on the optimal selection of parameters.To address the current challenges in parameter calibration for the Muskingum model,such as complex solution processes and low accuracy,the use of the Harris Hawks optimization(HHO)algorithm was proposed to optimize its parameters.HHO algorithm has a wide range of global search capabilities,with fewer parameters to be adjusted.Taking Luohe River,a tributary of the Yellow River,as the research object,the generalized nonlinear Muskingum model was used to simulate the flood in the Yiyang-Baimasi section of the river.The parameters were optimized by employing the HHO algorithm,particle swarm optimization(PSO)algorithm,and ant colony optimization(ACO)algorithm,respectively.The results show that the generalized nonlinear Muskingum model based on the HHO algorithm achieved high simulation accuracy in the Yiyang-Baimasi section of the Luohe River,with a Min.SSD of 1 237 and the flood peak error(DPO)of only 5,outperforming those obtained through optimization using PSO algorithm and ACO algorithm.The results are suitable for application in flood forecasting in the Yiyang-Baimasi section of the Luohe River.关键词
洪水预报/广义非线性马斯京根模型/哈里斯鹰算法/参数率定Key words
flood forecasting/generalized nonlinear Muskingum model/Harris Hawks optimization(HHO)algorithm/parameter calibration分类
建筑与水利引用本文复制引用
陈海涛,赵志杰..哈里斯鹰算法在广义非线性马斯京根参数优化中的应用[J].人民珠江,2024,45(2):60-68,9.基金项目
河南省科技攻关项目(222102320333) （222102320333）
