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基于粒子群算法的水文模型参数多目标优化研究
引用本文:张文明,董增川,朱成涛,钱蔚. 基于粒子群算法的水文模型参数多目标优化研究[J]. 水利学报, 2008, 39(5): 528-534
作者姓名:张文明  董增川  朱成涛  钱蔚
作者单位:河海大学,水文水资源与水利工程科学国家重点实验室,江苏,南京,210098;二滩水电开发有限责任公司,四川,成都,610021;河海大学环境科学与工程学院,江苏,南京,210098
基金项目:教育部科学技术研究重点项目
摘    要:在改进的粒子群算法基础上通过引入存档群体和拥挤距离机制,建立了基于粒子群算法的多目标算法,并将该算法应用于新安江模型参数多目标优化计算中,得到了最优解的Pareto集合.通过多目标距离函数法从Pareto集中求出一组单一解.将多目标优选的结果与单目标优化结果进行比较分析.结果表明,多目标参数优选方法综合考虑了水文过程的各种要素,比单目标优选结果具有更高的模拟精度.

关 键 词:粒子群箅法  水文模型  多目标  参数优化
文章编号:0559-9350(2008)05-0528-07
修稿时间:2007-09-13

Automatic calibration of hydrologic model based on multi objective particle swarm optimization method
ZHANG Wen ming. Automatic calibration of hydrologic model based on multi objective particle swarm optimization method[J]. Journal of Hydraulic Engineering, 2008, 39(5): 528-534
Authors:ZHANG Wen ming
Affiliation:Hohai University, Nanjing 210098, China
Abstract:An approach that extends the particle swarm optimization (PSO) algorithm to deal with the multi objective optimization problems by incorporating the mechanism of crowding distance computation and external archive of non dominated solution is presented. The proposed method is applied to automatically calibrate the parameters of Xinanjiang model and to find a set of Pareto optimal solution. Based on this set of Pareto optimal solution a single solution can be obtained by using a balanced aggregated objective function. Comparing with the single objective optimization by PSO, the proposed method can consider different aspects of the hydrograph and has better performance both in calibration and validation.
Keywords:particle swarm optimization   hydrologic model   multiobjective   automatic calibration
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