Simple, efficient and robust techniques for automatic multi-objective function parameterisation: Case studies of local and global optimisation using APSIM
Simple, efficient and robust techniques for automatic multi-objective function parameterisation: Case studies of local and global optimisation using APSIM
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DOI：
10.1016/j.envsoft.2019.03.010
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发表时间：
2019-07-01
影响因子：
4.9
通讯作者：
Zavattaro, Laura
中科院分区：
文献类型：
--
作者：
Harrison, Matthew Tom;Roggero, Pier Paolo;Zavattaro, Laura
使用PEST软件探索了几种自动参数化技术。我们参数化的生物物理系统模型APSIM与测量玉米种植实验的目标，找到算法，导致在最短的时间内建模和测量数据（φ）之间的最小距离。APSIM参数进行了优化，使用加权最小二乘法，最大限度地减少phi值。优化技术包括Gauss-Marquardt-Levenberg（GML）算法、奇异值分解（SVD）、最小二乘QR分解（LSQR）、Tikhonov正则化和协方差矩阵自适应进化策略（CMAES）。一般而言，具有对数变换的APSIM参数和较大群体大小的CMAES导致最低phi，但与其他优化算法相比，这种方法需要显著更长的收敛时间。正则化治疗与对数转换参数也导致低phi值时，结合SVD或LSQR; LSQR治疗没有正则化往往收敛earliest.In除了几个PEST算法的分析，本研究提供了一个叙述如何在这里提出的方法可以推广和应用到其他模型。
Several techniques for automatic parameterisation are explored using the software PEST. We parameterised the biophysical systems model APSIM with measurements from a maize cropping experiment with the objective of finding algorithms that resulted in the least distance between modelled and measured data (phi) in the shortest possible time. APSIM parameters were optimised using a weighted least-squares approach that minimised the value of phi. Optimisation techniques included the Gauss-Marquardt-Levenberg (GML) algorithm, singular value decomposition (SVD), least squares with QR decomposition (LSQR), Tikhonov regularisation, and covariance matrix adaptation-evolution strategy (CMAES).In general, CMAES with log transformed APSIM parameters and larger population size resulted in the lowest phi, but this approach required significantly longer to converge compared with other optimisation algorithms. Regularisation treatments with log transformed parameters also resulted in low phi values when combined with SVD or LSQR; LSQR treatments with no regularisation tended to converge earliest.In addition to an analysis of several PEST algorithms, this study provides a narrative on how methodologies presented here could be generalised and applied to other models.