中文摘要：

对粒子群算法进行了详细描述和分析，并将其应用于新安江模型的参数优选中．对于人工生成的理想水文资料，采用粒子群算法优化新安江模型，可以使全部参数收敛到真值；对于实测的水文资料，通过与单纯形混合加速遗传算法（SAGA）和单纯多边形进化算法（SCE-UA）进行比较，可以看出，粒子群算法全局收敛性能较好，计算效率和精度较高，是一种有效的新安江模型参数优选方法。

英文摘要：

Particle swarm optimization （PSO） is described and analyzed; and it is applied to calibrate Xin＇anjiang model. For the ideal hydrological data generated by the hydrological model, the real value of all the parameters of Xin＇anjiang model can be obtained by PSO. And for the practical data, we compare it with simplex hybrid accelerating genetic algorithm （SAGA） and shuffled complex evaluation algorithm （SCE-UA）. The results show that PSO has global convergence, higher efficiency and precision. It is an effective global optimization to calibrate hydrologic model.