期刊
ENVIRONMENTAL MODELLING & SOFTWARE
卷 77, 期 -, 页码 122-142出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2015.12.008
关键词
Global optimization; Meta-models; Radial basis functions; Hydrological calibration; Multi-reservoir management; Synthetic data
In water resources optimization problems, the objective function usually presumes to first run a simulation model and then evaluate its outputs. However, long simulation times may pose significant barriers to the procedure. Often, to obtain a solution within a reasonable time, the user has to substantially restrict the allowable number of function evaluations, thus terminating the search much earlier than required. A promising strategy to address these shortcomings is the use of surrogate modeling techniques. Here we introduce the Surrogate-Enhanced Evolutionary Annealing-Simplex (SEEAS) algorithm that couples the strengths of surrogate modeling with the effectiveness and efficiency of the evolutionary annealing-simplex method. SEEAS combines three different optimization approaches (evolutionary search, simulated annealing, downhill simplex). Its performance is benchmarked against other surrogate-assisted algorithms in several test functions and two water resources applications (model calibration, reservoir management). Results reveal the significant potential of using SEEAS in challenging optimization problems on a budget. (C) 2015 Elsevier Ltd. All rights reserved.
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