气候变化领域集成服务门户(CCPortal): Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules

CCPortal

DOI	10.5194/hess-24-3189-2020
	Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules - The caRamel algorithm
	Monteil C.; Zaoui F.; Le Moine N.; Hendrickx F.
发表日期	2020
ISSN	1027-5606
起始页码	3189
结束页码	3209
卷号	24 期号:6
英文摘要	Environmental modelling is complex, and models often require the calibration of several parameters that are not able to be directly evaluated from a physical quantity or field measurement. Multi-objective calibration has many advantages such as adding constraints in a poorly constrained problem or finding a compromise between different objectives by defining a set of optimal parameters. The caRamel optimizer has been developed to meet the requirement for an automatic calibration procedure that delivers not just one but a family of parameter sets that are optimal with regard to a multi-objective target. The idea behind caRamel is to rely on stochastic rules while also allowing more "local" mechanisms, such as the extrapolation along vectors in the parameter space. The caRamel algorithm is a hybrid of the multi-objective evolutionary annealing simplex (MEAS) method and the non-dominated sorting genetic algorithm II ("-NSGA-II). It was initially developed for calibrating hydrological models but can be used for any environmental model. The caRamel algorithm is well adapted to complex modelling. The comparison with other optimizers in hydrological case studies (i.e. NSGA-II and MEAS) confirms the quality of the algorithm. An R package, caRamel, has been designed to easily implement this multi-objective algorithm optimizer in the R environment. © Author(s) 2020.
语种	英语
scopus关键词	Calibration; Genetic algorithms; Stochastic systems; Vector spaces; Automatic calibration; Environmental model; Environmental modelling; Hydrological models; Multi objective algorithm; Multi-objective calibration; Multi-objective evolutionary; Non-dominated sorting genetic algorithm - ii; Parameter estimation; automation; calibration; environmental modeling; genetic algorithm; optimization; parameterization; stochasticity
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159372
作者单位	Monteil, C., EDF RandD LNHE - Laboratoire National d'Hydraulique et Environnement, Chatou, 78400, France; Zaoui, F., EDF RandD LNHE - Laboratoire National d'Hydraulique et Environnement, Chatou, 78400, France; Le Moine, N., UMR 7619 Metis (SU/CNRS/EPHE), Sorbonne Université, 4 Place Jussieu, Paris, 75005, France; Hendrickx, F., EDF RandD LNHE - Laboratoire National d'Hydraulique et Environnement, Chatou, 78400, France
推荐引用方式 GB/T 7714	Monteil C.,Zaoui F.,Le Moine N.,et al. Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules - The caRamel algorithm[J],2020,24(6).
APA	Monteil C.,Zaoui F.,Le Moine N.,&Hendrickx F..(2020).Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules - The caRamel algorithm.Hydrology and Earth System Sciences,24(6).
MLA	Monteil C.,et al."Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules - The caRamel algorithm".Hydrology and Earth System Sciences 24.6(2020).