基于随机分形搜索算法的马斯京根模型参数优选

Parameters Optimization of Muskingum Model Based on Stochastic Fractal Search

马斯京根模型在河道洪水演算中发挥着重要作用,该模型参数优选对提高洪水演算准确性至关重要。提出利用随机分形搜索算法(SFS)解决非线性马斯京根模型参数优选问题,同时将混沌序列替代SFS更新操作中的随机数。对算例进行洪水演算仿真分析并与多种优化算法比较,结果表明,随机分形搜索算法对非线性马斯京根法模型参数优选问题求解行之有效,且算法实施过程简便、参数解算精度高。

Muskingum model plays an important role in flood routing.The optimization choice of model＇s parameters has impact on improving the precision of flood routing.The stochastic fractal search algorithm was proposed to solve the parameters optimization of nonlinear Muskingum model.At the same time,random numbers were replaced by chaotic sequences for updating operation in stochastic fractal search.Compared with other optimization algorithms,the simulation results show that the proposed stochastic fractal search is an effective method for solving parameters optimization of nonlinear Muskingum model,and the implementation process is simple as well as the precision of the obtained parameters is higher.