摘要
鉴于在不同分布线型下,传统参数估计方法需要推导繁琐的公式,因而引入智能优化算法。研究新型智能优化算法估算年降水量系列频率分布参数,提高降水系列分布参数估计精度。以陕西省眉县、凤县和凤翔3个气象站的年降水量系列资料为例,应用反向学习自适应差分进化算法(OL-ADE)、蜻蜓算法(DA)和基于压缩因子的遗传粒子群混合算法(HGAPSO)原理,按照优化准则进行分布参数优化求解。在此基础上,利用TOPSIS评价法直观定量评价以上3种参数估计方法和3种传统参数估计方法。结果表明:与传统算法相比,3种新型智能优化算法在年降水量频率分布参数估计中均取得较好的拟合效果。HGAPSO算法拟合精度最高,DA和OL-ADE算法拟合效果大致相同。
To calculate the frequency distribution parameters of annual precipitation with different frequency distributions,traditional estimation methods need to derive complicated formulas.This paper uses new intelligent optimization algorithms for estimating such parameters to improve estimation accuracy.Three algorithms,self-adaptive differential evolution algorithm based on opposition-based learning(OL-ADE),dragonfly algorithm(DA),and hybrid genetic and particle swarm algorithm(HGAPSO),are applied to calculation of the distribution parameters according to the optimization criteria for the annual precipitation series of the meteorological stations of Meixian,Fengxian,and Fengxiang in Shaanxi.And estimation results of the three above mentioned methods and three traditional methods are evaluated using the technique for order preference by similarity to ideal solution(TOPSIS).The results show that compared to traditional methods,three intelligent optimization algorithms fit better with the annual precipitation frequency distribution parameters,the accuracy of HGAPSO is best,and DA and OL-ADE are comparable.
作者
王博
宋松柏
夏积德
何灏川
WANG Bo;SONG Songbai;XIA Jide;HE Haochuan(College of Water Resources and Architectural Engineering,Northwest A&F University,Yangling,Shaanxi 712100)
出处
《水力发电学报》
EI
CSCD
北大核心
2019年第12期49-60,共12页
Journal of Hydroelectric Engineering
基金
国家自然科学基金项目(51479171
51579059
51179160)
关键词
参数估计
反向学习自适应差分进化算法
蜻蜓算法
遗传粒子群混合算法
TOPSIS评价法
parameter estimation
self-adaptive differential evolution algorithm based on opposition-based learning
dragonfly algorithm
hybrid genetic and particle swarm algorithm
TOPSIS evaluation method
作者简介
王博(1995-),女,硕士生.E-mail:2738713737@qq.com;通信作者:宋松柏(1965-),男,教授.E-mail:ssb6533@nwsuaf.edu.cn。
