| 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. |
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