基于i-Alt-NSGA-Ⅱ-aJG算法的HBV模型参数优化及其应用

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英文篇名：Application of i-Alt-NSGA-Ⅱ-aJG Algorithm in Parameter Optimization of HBV Model 作者：代旭 ; 陈元芳 英文作者：DAI Xu;CHEN Yuan-fang;College of Hydrology and Water Resources,Hohai University; 关键词：HBV模型 ; 参数优化 ; i-NSGA-Ⅱ多目标优化算法 ; NSGA-Ⅱ多目标优化算法 ; i-Alt-NSGA-Ⅱ-aJG算法 英文关键词：HBV model;;parameter optimization;;i-NSGA-Ⅱ multi-objective optimization algorithm;;NSGA-Ⅱ multi-objective optimization algorithm;;i-Alt-NSGA-Ⅱ-aJG algorithm 中文刊名：SDNY 英文刊名：Water Resources and Power 机构：河海大学水文水资源学院; 出版日期：2018-06-25 出版单位：水电能源科学 年：2018 期：v.36;No.214 基金：国家自然科学基金项目(51479061) 语种：中文; 页：SDNY201806004 页数：4 CN：06 ISSN：42-1231/TK 分类号：20-22+115

摘要

针对种群数量众多且参数率定耗时的问题,对比分析了具有继承性的NSGA-Ⅱ(i-NSGA-Ⅱ)算法与NSGA-Ⅱ算法及两者考虑跳跃基因的相关算法的收敛性能,验证了基因继承性对算法性能的提升,从中选出最优算法。利用尼泊尔巴格马蒂河流域2000~2004年实测洪水径流过程资料对HBV模型进行参数率定,得出Pareto最优解,并利用2005年5场洪水日径流过程进行模型检验。结果表明,i-NSGA-Ⅱ算法优于相对应的NSGA-Ⅱ算法,从中选出的最优i-Alt-NSGA-Ⅱ-aJG算法能够最快地得到最优解且解的质量较好,表明i-Alt-NSGA-Ⅱ-aJG优化算法在解决多参数多目标优化问题中具有优势。
        Aiming at a large number of populations and time-consuming of parameters calibration,the convergence performance of i-NSGA-II and NSGA-II as well as their jumping gene adaptations were compared and analyzed.The gene inheritance to improve algorithm was verified for selecting the best algorithm(i-Alt-NSGA-II-aJG).The observed daily discharge data of Marty River Basin in Nepal was used to construct HBV hydrological model,and i-Alt-NSGA-II-aJG was adopted to get Pareto optimal solution.The model was tested by using 5 daily floods series in 2005.The results show that the i-NSGA-II algorithm is superior to NSGA-II.The best selected i-Alt-NSGA-II-aJG algorithm has the fastest speed for obtaining optimal solutions and its quality is good.It shows that the i-Alt-NSGA-II-aJG has the advantage in solving multi-parameter and multi-objective optimization problems.

引文

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