Adaptive efficient global optimization of systems with independent components
Tóm tắt
We present a novel approach for efficient optimization of systems consisting of expensive to simulate components and relatively inexpensive system-level simulations. We consider the types of problem in which the components of the system problem are independent in the sense that they do not exchange coupling variables, however, design variables can be shared across components. Component metamodels are constructed using Kriging. The metamodels are adaptively sampled based on a system level infill sampling criterion using Efficient Global Optimization. The effectiveness of the technique is demonstrated by applying it on numerical examples and an engineering case study. Results show steady and fast converge to the global deterministic optimum of the problems.
Tài liệu tham khảo
Ahmed OS, Swillam MA, Bakr MH, Li X (2011) Efficient Design Optimization of Ring Resonator-Based Optical Filters. J Lightwave Technol 29(18):2812–2817. doi:10.1109/JLT.2011.2162818
Ayyub BM (2003) Risk Analysis in Engineering and Economics. Chapman & Hall/CRC, Boca Raton, FL
Barton RR (1997) Design of experiments for fitting subsystem metamodels Proceedings of the 29th conference on Winter simulation, IEEE Computer Society, pp 303–310
Forrester A, Sobester A, Keane A (2008) Engineering design via surrogate modelling: a practical guide. John Wiley & Sons, UK
Hu W, Li M, Azarm S, Almansoori A (2011) Multi-Objective Robust Optimization Under Interval Uncertainty Using Online Approximation and Constraint Cuts. J Mech Des 133(6):061,002. doi:10.1115/1.4003918
Jones DR, Schonlau M, Welch WJ (1998) Efficient Global Optimization of Expensive Black-Box Functions. J Glob Optim 13(4):455–492. doi:10.1023/A:1008306431147
Kleijnen JP (2015) Design and analysis of simulation experiments. Springer
Kleijnen JP, Mehdad E (2014) Multivariate versus univariate kriging metamodels for multi-response simulation models. Eur J Oper Res 236(2):573–582. doi:10.1016/j.ejor.2014.02.001
Koch PN, Mavris D, Mistree F (2000) Partitioned, Multilevel Response Surfaces for Modeling Complex Systems. AIAA J 38(5):875–881. doi:10.2514/2.1042
Kokkolaras M, Mourelatos ZP, Papalambros PY (2004) Design optimization of hierarchically decomposed multilevel systems under uncertainty ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers, p 613–624
Li G, Azarm S, Farhang-Mehr A, Diaz AR (2006) Approximation of multiresponse deterministic engineering simulations: a dependent metamodeling approach. Struct Multidiscip Optim 31(4):260–269. doi:10.1007/s00158-005-0574-5
Lifante G (2003) Integrated photonics: fundamentals. J. Wiley. doi:10.1002/0470861401
Martins JRRA, Lambe AB (2013) Multidisciplinary Design Optimization: A Survey of Architectures. AIAA J 51(9):2049–2075. doi:10.2514/1.J051895
Mühlenbein H, Schomisch M, Born J (1991) The parallel genetic algorithm as function optimizer. Parallel Comput 17(6):619–632. doi:10.1016/S0167-8191(05)80052-3
Sacks J, Welch W, Mitchell TJ, Wynn HP (1989) Design and Analysis of Computer Experiments. Stat Sci 4(4):409–435
Schonlau M (1997) Computer experiments and global optimization. PhD thesis, University of Waterloo
Schonlau M, Welch W (1996) Global optimization with nonparametric function fitting Proceedings of the Section on Physical and Engineering Sciences, American Statistical Association, pp 183–186
Surjanovic S, Bingham D (2013) Virtual Library of Simulation Experiments: Test Functions and Datasets. http://www.sfu.ca/ssurjano
Syms R, Cozens J (1992) Optical guided waves and devices. McGraw-Hill
Taboga M (2012) Lectures on probability theory and mathematical statistics., 2nd edn. CreateSpace Independent Publishing Platform
Viana FAC, Simpson TW, Balabanov V, Toropov V (2014) Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? AIAA J 52(4):670–690. doi:10.2514/1.J052375
Xiu R, Zhang X, Liu Y, Huang HZ (2013) Metamodeling uncertainty quantification in multi-level engineering system design Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE), 2013 International Conference on, pp 449–454. doi:10.1109/QR2MSE.2013.6625621