多元优化算法在马斯京根模型参数优化中的应用  

Application of multi-verse optimization algorithm in parameter optimization of muskingum method

作　　者：李小波 

机构地区：[1]云南省水利水电勘测设计研究院,云南昆明650021

出　　处：《水资源与水工程学报》2016年第5期158-161,共4页Journal of Water Resources and Water Engineering

基　　金：国家水体污染控制与治理科技重大专项(201307102-006-01);院士工作站建设专项(2015IC013)

摘　　要：以2个实例为研究对象,利用一种新型群体智能算法——多元优化(MVO)算法优化马斯京根模型参数,并与相关文献中加速遗传算法等多种方法的优化结果进行对比。结果表明:MVO算法优化结果优于其他算法,利用MVO算法优化马斯京根模型参数,可以获得比相关文献更高的模拟精度,不但为精确估计马斯京根模型参数提供了有效方法,而且拓展了MVO算法在水文模型参数优化中的应用。Taking 2 cases as the research object,the paper used a novel swarm intelligence algorithm--multiple optimization（ MVO） algorithm to optimize the parameters of Muskingum model,and compared the results with that of accelerating genetic algorithm and other methods in related literature. The results show that MVO algorithm is better than the other algorithm. By use of MVO algorithm to optimize the parameter of Muskingum model can get higher precision than that of related literature. The mathod can not only provide an effective method for accurate parameter estimation of Muskingum model,but also expand the application of MVO algorithm in parameter optimization of hydrological model.

关 键 词：多元优化算法 马斯京根模型 参数优化 河道洪水演算 

分 类 号：TV131.61[水利工程—水力学及河流动力学]