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- W3091635118 abstract "Engineering optimization problems often involve computationally expensive black-box simulations of underlying physical phenomena. This paper compares the performance of four constrained optimization algorithms relying on a Gaussian process model and an infill sampling criterion under the framework of Bayesian optimization. The four infill sampling criteria include expected feasible improvement (EFI), constrained expected improvement (CEI), stepwise uncertainty reduction (SUR), and augmented Lagrangian (AL). Numerical tests were rigorously performed on a benchmark set consisting of nine constrained optimization problems with features commonly found in engineering, as well as a constrained structural engineering design optimization problem. Based upon several measures including statistical analysis, our results suggest that, overall, the EFI and CEI algorithms are significantly more efficient and robust than the other two methods, in the sense of providing the most improvement within a very limited number of objective and constraint function evaluations, and also in the number of trials for which a feasible solution could be located." @default.
- W3091635118 created "2020-10-08" @default.
- W3091635118 creator A5044943277 @default.
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- W3091635118 date "2020-10-03" @default.
- W3091635118 modified "2023-09-24" @default.
- W3091635118 title "A Comparative Study of Infill Sampling Criteria for Computationally Expensive Constrained Optimization Problems" @default.
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- W3091635118 doi "https://doi.org/10.3390/sym12101631" @default.
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