摘要
水文模型参数的优化对模拟结果至关重要,参数率定方法中的多目标分析方法备受关注。将具有继承性的精英非支配排序遗传算法(i-NSGA-Ⅱ)与多目标涡流粒子种群算法(MOVPSO)分别作为NSGA-Ⅱ算法与PSO算法的改进算法,与AMS算法及DE算法共同寻优,提出改进的遗传自适应多目标算法(AMALGAM算法),以超体积、收敛性度量及多样性度量作为算法性能评价指标,对比改进的AMALGAM算法与AMALGAM算法的解集性能。选出较优算法,并结合实例对基于地形指数的水文模型(TOPMODEL)进行参数率定,得出Pareto最优解。结果表明改进的AMALGAM算法优于AMALGAM算法,模型预报精度较高,在解决多参数多目标优化问题中具有优势。
Parameters optimization of hydrological model plays an important role in simulation results.Multi-objective analysis of parameters calibration has been paid more and more attention.The i-NSGA-II and MOVPSO were taken as modified algorithms in NSGA-II and PSO.Combining with AMS and DE,a modified genetically adaptive multiobjective method was proposed.The modified AMALGAM was compared with AMALGAM in convergence performance on the basis of hyper-volume,convergence metric and diversity metric.The better algorithm was chosen to calibrate parameters of TOPMODEL based terrain index,and the Pareto optimal solution was obtained.Example results show that the modified AMALGAM outperforms the AMALGAM,the model＇s forecasting accuracy is high.So,the modified AMALGAM algorithm has advantages in solving multi-objective parameters optimization problems.
作者
代旭
陈元芳
DAI Xu;CHEN Yuan-fang(College of Hydrology and Water Resources,Hohai University,Nanjing 210098,China)
出处
《水电能源科学》
北大核心
2018年第8期14-17,共4页
Water Resources and Power
基金
国家自然科学基金项目(51479061)
