Abstract
Sequential surrogate model-based global optimization algorithms, such as super-EGO, have been developed to increase the efficiency of commonly used global optimization technique as well as to ensure the accuracy of optimization. However, earlier studies have drawbacks because there are three phases in the optimization loop and empirical parameters. We propose a united sampling criterion to simplify the algorithm and to achieve the global optimum of problems with constraints without any empirical parameters. It is able to select the points located in a feasible region with high model uncertainty as well as the points along the boundary of constraint at the lowest objective value. The mean squared error determines which criterion is more dominant among the infill sampling criterion and boundary sampling criterion. Also, the method guarantees the accuracy of the surrogate model because the sample points are not located within extremely small regions like super-EGO. The performance of the proposed method, such as the solvability of a problem, convergence properties, and efficiency, are validated through nonlinear numerical examples with disconnected feasible regions.
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Recommended by Guest Editor Joo-Ho Choi
Su-gil Cho received Ph.D. degree in Automotive Engineering from the Hanyang University, Korea. He is currently a researcher at KRISO (Korea Research Institute of Ships& Ocean engineering), Korea. His research interests include design and analysis of computer experiments, uncertainty-based multidisciplinary design optimization, and surrogate model based optimization.
Tae Hee Lee received Ph.D. degree at the University of Iowa in 1991 under supervision of Prof. J.S. Aroa. At various times during his career, he has held appointments at the University of Iowa in USA, Tokyo Denki University in Japan, Yeoungnam University in Korea, and Georgia Institute of Technology in USA. He received an award for excellence in academic achievement in 2013 from Korean Society for Mechanical Engineers. His research interests include design optimization, design and analysis of computer experiments, design under uncertainty, and surrogate model based optimization.
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Cho, Sg., Jang, J., Kim, J. et al. Statistical surrogate model based sampling criterion for stochastic global optimization of problems with constraints. J Mech Sci Technol 29, 1421–1427 (2015). https://doi.org/10.1007/s12206-015-0313-9
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DOI: https://doi.org/10.1007/s12206-015-0313-9