Optimization of the Hydrological Model Using Multi-objective Particle Swarm Optimization Algorithm

被引：4
|
作者：
黄晓敏 [1 ,2 ]
雷晓辉 [2 ]
王宇晖 [1 ]
朱连勇 [3 ]
机构：
[1] School of Environmental Science and Engineering,Donghua University
[2] State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin,China Institute of Water Resources and Hydropower Research
[3] College of Water Conservancy and Civil Engineering,Xinjiang Agricultural University
来源：
Journal of Donghua University(English Edition) | 2011年 / 28卷 / 05期
关键词：
multi-objective particle swarm optimization(MOPSO); hydrological model(HYMOD); multi-objective optimization;
D O I：
10.19884/j.1672-5220.2011.05.017
中图分类号：
P334.92 [];
学科分类号：
081501 ;
摘要：
An application of multi-objective particle swarm optimization（MOPSO） algorithm for optimization of the hydrological model（HYMOD） is presented in this paper.MOPSO algorithm is used to find non-dominated solutions with two objectives: high flow Nash-Sutcliffe efficiency and low flow Nash-Sutcliffe efficiency.The two sets’ coverage rate and Pareto front spacing metric are two criterions to analyze the performance of the algorithms.MOPSO algorithm surpasses multi-objective shuffled complex evolution metropolis（MOSCEM;A） algorithm in terms of the two sets’ coverage rate.But when we come to Pareto front spacing rate,the non-dominated solutions of MOSCEM;A algorithm are better-distributed than that of MOPSO algorithm when the iteration is set to 40000.In addition,there are obvious conflicts between the two objectives.But a compromise solution can be acquired by adopting the MOPSO algorithm.
引用
收藏
页码：519 / 522
页数：4
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