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An evaluation of adaptive surrogate modeling based optimization 
with two benchmark problems

 

Chen Wang, Qingyun Duan*, Wei Gong, Aizhong Ye, Zhenhua Di, Chiyuan Miao

 

State Key Laboratory for Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University,Beijing 100875, China

 

ABSTRACT

Surrogate modeling uses cheap “surrogates” to represent the response surface of simulation models. It involves several steps, including initial sampling, regression and adaptive sampling. This study evaluates an adaptive surrogate modeling based optimization (ASMO) method on two benchmark problems: the Hartman function and calibration of the SAC-SMA hydrologic model. Our results show that: 1) Gaussian Processes are the best surrogate model construction method. A minimum Interpolation Surface method is the best adaptive sampling method. Low discrepancy Quasi Monte Carlo methods are the most suitable initial sampling designs. Some 15–20 times the dimension of the problem may be the proper initial sample size; 2) The ASMO method is much more efficient than the widely used Shuffled Complex Evolution global optimization method. However, ASMO can provide only approximate optimal solutions, whose precision is limited by surrogate modeling methods and problem-specific features; and 3) The identifiability of model parameters is correlated with parameter sensitivity.

 

KEY WORDS: Adaptive surrogate modeling based optimization; Design of experiment; Adaptive sampling; Global sensitivity analysis; Computationally intensive computer models

 

PUBLISHED BY: ENVIRONMENTAL MODELLING & SOFTWARE, 2014, 60: 167-179

 

 

SOURCE:  http://www.sciencedirect.com/science/article/pii/S1364815214001698