The Strategic Random Search (SRS) - A new global optimizer for calibrating hydrological models

被引:2
|
作者
Wei, Haoshan [1 ,2 ]
Zhang, Yongqiang [1 ]
Liu, Changming [1 ]
Huang, Qi [1 ,2 ]
Jia, Pengxin [1 ]
Xu, Zhenwu [1 ,2 ]
Guo, Yuhan [1 ,2 ]
机构
[1] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
关键词
Global optimizer; Unconstrained single-objective function; Strategic Random Search; Model calibration; Rainfall-runoff models; DATA ASSIMILATION; UNCERTAINTY; EVOLUTION;
D O I
10.1016/j.envsoft.2023.105914
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This study introduces a novel global optimization algorithm, Strategic Random Search (SRS), tailored for effi-cient calibration of hydrological models. SRS outperforms 14 other optimization algorithms on 23 classical benchmark functions and 29 CEC-2017 benchmark functions, demonstrating its superiority on more than hal f of these tests. Additionally, when applied to rainfall-runof f models, SRS consistently, rapidly, and robustly con-verges to optimal solutions, surpassing five other algorithms. SRS, developed independently of existing intelli-gent optimization methods, offers versatility with only two adjustable parameters, making it suitable for various problem types. Through rigorous testing and comparisons, SRS emerges as a robust, widely applicable, and stable convergence algorithm.
引用
收藏
页数:23
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