This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Comparative Benchmark Studies of Response Surface Model-Based Optimization and Direct Multidisciplinary Design Optimization
Technical Paper
2014-01-0400
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
Response Surface Model (RSM)-based optimization is widely used in engineering design. The major strength of RSM-based optimization is its short computational time. The expensive real simulation models are replaced with fast surrogate models. However, this method may have some difficulties to reach the full potential due to the errors between RSM and the real simulations. RSM's accuracy is limited by the insufficient number of Design of Experiments (DOE) points and the inherent randomness of DOE. With recent developments in advanced optimization algorithms and High Performance Computing (HPC) capability, Direct Multidisciplinary Design Optimization (DMDO) receives more attention as a promising future optimization strategy. Advanced optimization algorithm reduces the number of function evaluations, and HPC cut down the computational turnaround time of function evaluations through fully utilizing parallel computation. In this paper, we test the performance of RSM-based optimization and DMDO using multiple benchmark problems of both analytical mathematical examples and a vehicle design. The benchmark problems cover three different scenario of using RSM-based optimization: (1) only real objective function is replaced with RSM; (2) only real constraint functions are replaced with RSM, and (3) both objective and constraint functions are replaced with RSM. The results are compared to DMDO to give recommendations of optimization method choices for different types of design problems with three criterions in performance, feasibility, and efficiency. This work provides systematic benchmark studies to help answer the following research questions: when the RSM-based method or DMDO should be chosen, and how to evaluate the performance of optimization algorithm.
Recommended Content
Authors
Citation
Xu, H., Majcher, M., Chuang, C., Fu, Y. et al., "Comparative Benchmark Studies of Response Surface Model-Based Optimization and Direct Multidisciplinary Design Optimization," SAE Technical Paper 2014-01-0400, 2014, https://doi.org/10.4271/2014-01-0400.Also In
References
- Kolda T. G. , Lewis R. M. , and Torczon V. Optimization by direct search: New perspectives on some classical and modern methods Siam Review 45 385 482 Sep 2003
- Hill W. J. and Hunter W. G. A Review of Response Surface Methodology - a Literature Survey Technometrics 8 571 1966
- Jin R. , Chen W. , and Simpson T. W. Comparative studies of metamodelling techniques under multiple modelling criteria Structural and Multidisciplinary Optimization 23 1 13 Dec 2001
- Simpson T. W. , Peplinski J. D. , Koch P. N. , and Allen J. K. Metamodels for computer-based engineering design: survey and recommendations Engineering with Computers 17 129 150 2001
- Hardy R. L. Multiquadric Equations of Topography and Other Irregular Surfaces Journal of Geophysical Research 76 1905 1971
- Dyn N. , Levin D. , and Rippa S. Numerical Procedures for Surface Fitting of Scattered Data by Radial Functions Siam Journal on Scientific and Statistical Computing 7 639 659 Apr 1986
- Powell M. J. Radial basis functions for multivariable interpolation: a review Algorithms for approximation Mason J. C. and Cox M. G. London Oxford University Press 1987 143 167
- Fang H. , Rais-Rohani M. , Liu Z. , and Horstemeyer M. F. A comparative study of metamodeling methods for multiobjective crashworthiness optimization Computers & Structures 83 2121 2136 Sep 2005
- Van der Velden A. S. I. M. U. L. I. A. Director Isight Design Optimization Methodologies ASM Handbook 22 2010
- Poles S. , Rigoni E. , and Robic T. MOGA-II performance on noisy optimization problems International Conference on Bioinspired Optimization Methods and their applications BIOMA Ljubljana, Slovena 2004
- Chase N. , Rademacher M. , Goodman E. , Averill R. , and Sidhu R. A Benchmark Study of Multi-Objective Optimization Methods
- Zitzler E. , Deb K. , and Thiele L. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results Evolutionary Computation 8 173 195 Sum 2000
- Hassan R. , Cohanim B. , De Weck O. , and Venter G. A comparison of particle swarm optimization and the genetic algorithm 2005
- Mehrabian A. R. and Lucas C. A novel numerical optimization algorithm inspired from weed colonization Ecological Informatics 1 355 366 Dec 2006
- Elbeltagi E. , Hegazy T. , and Grierson D. Comparison among five evolutionary-based optimization algorithms Advanced Engineering Informatics 19 43 53 Jan 2005
- Panda S. and Padhy N. P. Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design Applied Soft Computing 8 1418 1427 Sep 2008
- Back T. Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms 996 Oxford Oxford university press 1996