In this study, the hybrid particle swarm optimization (HPSO) algorithm was proposed and practised for the calibration of two conceptual rainfall–runoff models (dynamic water balance model and abcde). The performance of the developed method was compared with those of several metaheuristics. The models were calibrated for three sub-basins, and multiple performance criteria were taken into consideration in comparison. The results indicated that HPSO was derived significantly better and more consistent results than other algorithms with respect to hydrological model errors and convergence speed. A variance decomposition-based method – analysis of variance (ANOVA) – was also used to quantify the dynamic sensitivity of HPSO parameters. Accordingly, the individual and interactive uncertainties of the parameters defined in the HPSO are relatively low.

在这项研究中，提出了混合粒子群优化（HPSO）算法，并将其用于校准两个概念性降雨-径流模型（动态水平衡模型和abcde））。将该开发方法的性能与几种元启发法进行了比较。针对三个子流域对模型进行了校准，并在比较中考虑了多个性能标准。结果表明，就水文模型误差和收敛速度而言，与其他算法相比，HPSO获得了更好，更一致的结果。基于方差分解的方法（方差分析（ANOVA））也用于量化HPSO参数的动态灵敏度。因此，HPSO中定义的参数的个体和交互不确定性相对较低。