非线性马斯京根模型参数率定的新方法

Parameter estimation of nonlinear Muskingum Model using mixed genetic algorithm

本文在分析经典二进制遗传算法不足之处的基础上提出一种改进的混合遗传算法 (MGA) ,用于对非线性马斯京根模型参数的估计 .同其它算法相比显示出求解精度高而且收敛速度快的特点 .通过具体仿真计算验证了该方法的正确性 。

Based on the analysis on disadvantage of the classical binary\|coded genetic algorithm,an improved genetic algorithm named mixed genetic algorithm(MGA) is proposed.The principle and implementing steps of this algorithm are introduced in detail and is applied to estimate the parameter of nonlinear Muskingum model.It improves the precision of solutions and accelerates the convergence.