Pramudita Satria Palar
ABSTRACT
Surrogate models are invaluable tools that greatly assist the process of computationally expensive analyses and optimization. Engineering optimization reaps the benefit from surrogate models in order to perform expensive optimization that could potentially be computationally intractable in the pre-high-performance computing age. Moreover, surrogate models provide a means to allow engineering design exploration with high-fidelity computer simulations. Despite their wide use and substantial research progresses, there are still some key issues and challenges that need to be addressed by researchers. Most of these issues stem from the growing complexity of engineering design optimization and exploration in real-world problems. In other words, the sophistication of the problem that we have to tackle increases faster than that of computing power and technology. It is thus imperative to have accurate and yet computationally efficient surrogate models that are suitable for real-world engineering problems. In this paper, we discuss key issues and challenges of the application of surrogate models in engineering design optimization and exploration. This paper is directed toward general readers, in which we aim to present general discussions regarding the effectiveness, issues, and future of surrogate-based optimization and exploration in engineering.
- Niels Aage, Erik Andreassen, Boyan S Lazarov, and Ole Sigmund. 2017. Gigavoxel computational morphogenesis for structural design. Nature 550, 7674 (2017), 84.Google Scholar
- Taimoor Akhtar and Christine A Shoemaker. 2016. Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection. Journal of Global Optimization 64, 1 (2016), 17--32. Google ScholarDigital Library
- Natalia M Alexandrov, JE Dennis, Robert Michael Lewis, and Virginia Torczon. 1998. A trust-region framework for managing the use of approximation models in optimization. Structural optimization 15, 1 (1998), 16--23.Google Scholar
- Natalia M Alexandrov and Robert Michael Lewis. 2001. An overview of first-order model management for engineering optimization. Optimization and Engineering 2, 4 (2001), 413--430.Google ScholarCross Ref
- Jan Backhaus, Andreas Schmitz, Christian Frey, MaxSagebaum, Sebastian Mann, Marc Nagel, and Nicolas R Gauger. 2017. Application of an algorithmically differentiated turbomachinery flow solver to the optimization of a fan stage. In 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. 3997.Google ScholarCross Ref
- Samineh Bagheri, Wolfgang Konen, Richard Allmendinger, Jürgen Branke, Kalyanmoy Deb, Jonathan Fieldsend, Domenico Quagliarella, and Karthik Sindhya. 2017. Constraint handling in efficient global optimization. In Proceedings of the Genetic and Evolutionary Computation Conference. ACM, 673--680. Google ScholarDigital Library
- Anirban Basudhar, Christoph Dribusch, Sylvain Lacaze, and Samy Missoum. 2012. Constrained efficient global optimization with support vector machines. Structural and Multidisciplinary Optimization 46, 2 (2012), 201--221. Google ScholarDigital Library
- Dimitri Bettebghor, Nathalie Bartoli, Stéphane Grihon, Joseph Morlier, and Manuel Samuelides. 2011. Surrogate modeling approximation using a mixture of experts based on EM joint estimation. Structural and multidisciplinary optimization 43, 2 (2011), 243--259. Google ScholarDigital Library
- Hans-Georg Beyer and Bernhard Sendhoff. 2007. Robust optimization-a comprehensive survey. Computer methods in applied mechanics and engineering 196, 33--34 (2007), 3190--3218.Google Scholar
- Barron J Bichon, Michael S Eldred, Sankaran Mahadevan, and John M McFarland. 2013. Efficient global surrogate modeling for reliability-based design optimization. Journal of Mechanical Design 135, 1 (2013), 011009.Google ScholarCross Ref
- Barron J Bichon, Michael S Eldred, Laura Painton Swiler, Sandaran Mahadevan, and John M McFarland. 2008. Efficient global reliability analysis for nonlinear implicit performance functions. AIAA journal 46, 10 (2008), 2459--2468.Google ScholarCross Ref
- Géraud Blatman and Bruno Sudret. 2011. Adaptive sparse polynomial chaos expansion based on least angle regression. J. Comput. Phys. 230, 6 (2011), 2345--2367. Google ScholarDigital Library
- Mohamed Amine Bouhlel, Nathalie Bartoli, Abdelkader Otsmane, and Joseph Morlier. 2016. Improving kriging surrogates of high-dimensional design models by Partial Least Squares dimension reduction. Structural and Multidisciplinary Optimization 53, 5 (2016), 935--952. Google ScholarDigital Library
- Roberto Calandra, André Seyfarth, Jan Peters, and Marc Peter Deisenroth. 2016. Bayesian optimization for learning gaits under uncertainty. Annals of Mathematics and Artificial Intelligence 76, 1--2 (2016), 5--23. Google ScholarDigital Library
- Tanmoy Chatterjee, Rajib Chowdhury, and Palaniappan Ramu. 2019. Decoupling uncertainty quantification from robust design optimization. Structural and Multidisciplinary Optimization (2019), 1--22. Google ScholarDigital Library
- Anirban Chaudhuri, Raphael T Haftka, Kelvin T Chang, Jordan Van Hall, and Peter Ifju. 2015. Multi-objective experimental optimization with multiple simultaneous sampling for flapping wings. In 56th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. 1586.Google Scholar
- Anirban Chaudhuri, Raphael T Haftka, Peter Ifju, Kelvin Chang, Christopher Tyler, and Tony Schmitz. 2015. Experimental flapping wing optimization and uncertainty quantification using limited samples. Structural and Multidisciplinary Optimization 51, 4 (2015), 957--970. Google ScholarDigital Library
- Mingjie Chen, Andrew FB Tompson, Robert J Mellors, and Osman Abdalla. 2015. An efficient optimization of well placement and control for a geothermal prospect under geological uncertainty. Applied energy 137 (2015), 352--363.Google Scholar
- Seung-Kyum Choi, Ramana V Grandhi, Robert A Canfield, and Chris L Pettit. 2004. Polynomial chaos expansion with latin hypercube sampling for estimating response variability. AIAA journal 42, 6 (2004), 1191--1198.Google ScholarCross Ref
- Tinkle Chugh, Nirupam Chakraborti, Karthik Sindhya, and Yaochu Jin. 2017. A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem. Materials and Manufacturing Processes 32, 10(2017), 1172--1178.Google ScholarCross Ref
- Tinkle Chugh, Yaochu Jin, Kaisa Miettinen, Jussi Hakanen, and Karthik Sindhya. 2018. A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization. IEEE Transactions on Evolutionary Computation 22, 1 (2018), 129--142.Google ScholarCross Ref
- Paul G Constantine, Eric Dow, and Qiqi Wang. 2014. Active subspace methods in theory and practice: applications to kriging surfaces. SIAM Journal on Scientific Computing 36, 4 (2014), A1500--A1524.Google ScholarCross Ref
- Steven J Daniels, Alma AM Rahat, Richard M Everson, Gavin R Tabor, and Jonathan E Fieldsend. 2018. A suite of computationally expensive shape optimisation problems using computational fluid dynamics. In International Conference on Parallel Problem Solving from Nature. Springer, 296--307.Google ScholarCross Ref
- JHS de Baar, TP Scholcz, and RP Dwight. 2015. Exploiting adjoint derivatives in high-dimensional metamodels. AIAA Journal 53, 5 (2015), 1391--1395.Google ScholarCross Ref
- Harris Drucker, Christopher JC Burges, Linda Kaufman, Alex J Smola, and Vladimir Vapnik. 1997. Support vector regression machines. In Advances in neural information processing systems. 155--161. Google ScholarDigital Library
- B Echard, N Gayton, and M Lemaire. 2011. AK-MCS: an active learning reliability method combining Kriging and Monte Carlo simulation. Structural Safety 33, 2 (2011), 145--154.Google ScholarCross Ref
- M Giselle Fernández-Godino, Chanyoung Park, Nam-Ho Kim, and Raphael T Haftka. 2016. Review of multi-fidelity models. arXiv preprint arXiv:1609.07196 (2016).Google Scholar
- Peter J Fleming, Robin C Purshouse, and Robert J Lygoe. 2005. Many-objective optimization: An engineering design perspective. In International conference on evolutionary multi-criterion optimization. Springer, 14--32. Google ScholarDigital Library
- Alexander IJ Forrester, András Sóbester, and Andy J Keane. 2006. Optimization with missing data. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 462, 2067 (2006), 935--945.Google ScholarCross Ref
- Adam Gaier, Alexander Asteroth, and Jean-Baptiste Mouret. 2017. Data-efficient exploration, optimization, and modeling of diverse designs through surrogate-assisted illumination. In Proceedings of the Genetic and Evolutionary Computation Conference. ACM, 99--106. Google ScholarDigital Library
- Javier González, Zhenwen Dai, Philipp Hennig, and Neil Lawrence. 2016. Batch bayesian optimization via local penalization. In Artificial Intelligence and Statistics. 648--657.Google Scholar
- H-M Gutmann. 2001. A radial basis function method for global optimization. Journal of global optimization 19, 3 (2001), 201--227. Google ScholarDigital Library
- Raphael T Haftka, Diane Villanueva, and Anirban Chaudhuri. 2016. Parallel surrogate-assisted global optimization with expensive functions-a survey. Structural and Multidisciplinary Optimization 54, 1 (2016), 3--13. Google ScholarDigital Library
- Zhong-Hua Han, Jing Chen, Ke-Shi Zhang, Zhen-Ming Xu, Zhen Zhu, and Wen-Ping Song. 2018. Aerodynamic shape optimization of natural-laminar-flowing using surrogate-based approach. AIAA Journal 56, 7 (2018), 2579--2593.Google ScholarCross Ref
- Zhong-Hua Han and Stefan Görtz. 2012. Hierarchical kriging model for variable-fidelity surrogate modeling. AIAA journal 50, 9 (2012), 1885--1896.Google ScholarCross Ref
- Kenneth Holmström. 2008. An adaptive radial basis algorithm (ARBF) for expensive black-box global optimization. Journal of Global Optimization 41, 3 (2008), 447--464. Google ScholarDigital Library
- Daniel Horn, Melanie Dagge, Xudong Sun, and Bernd Bischl. 2017. First Investigations on Noisy Model-Based Multi-Objective Optimization. In International Conference on Evolutionary Multi-Criterion Optimization. Springer, 298--313. Google ScholarDigital Library
- Nicholas J Horton and Ken P Kleinman. 2007. Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models. The American Statistician 61, 1 (2007), 79--90.Google ScholarCross Ref
- Chao Hu and Byeng D Youn. 2011. Adaptive-sparse polynomial chaos expansion for reliability analysis and design of complex engineering systems. Structural and Multidisciplinary Optimization 43, 3 (2011), 419--442. Google ScholarDigital Library
- Jorge E Hurtado and Diego A Alvarez. 2001. Neural-network-based reliability analysis: a comparative study. Computer methods in applied mechanics and engineering 191, 1--2 (2001), 113--132.Google Scholar
- Afzal Husain and Kwang-Yong Kim. 2010. Enhanced multi-objective optimization of a microchannel heat sink through evolutionary algorithm coupled with multiple surrogate models. Applied Thermal Engineering 30, 13 (2010), 1683--1691.Google ScholarCross Ref
- Hisao Ishibuchi, Noritaka Tsukamoto, and Yusuke Nojima. 2008. Evolutionary many-objective optimization: A short review. In 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). IEEE, 2419--2426.Google ScholarCross Ref
- Alexander I J. Forrester, Andy J Keane, and Neil W Bressloff. 2006. Design and analysis of" Noisy" computer experiments. AIAA journal 44, 10 (2006), 2331--2339.Google ScholarCross Ref
- Jan Pieter Jacobs, S Koziel, and S Ogurtsov. 2013. Computationally efficient multi-fidelity Bayesian support vector regression modeling of planar antenna input characteristics. IEEE Transactions on Antennas and Propagation 61, 2 (2013), 980--984.Google ScholarCross Ref
- Antony Jameson. 1988. Aerodynamic design via control theory. Journal of scientific computing 3, 3 (1988), 233--260. Google ScholarDigital Library
- Shinkyu Jeong, Kazuhisa Chiba, and Shigeru Obayashi. 2005. Data mining for aerodynamic design space. Journal of aerospace computing, information, and communication 2, 11 (2005), 452--469.Google ScholarCross Ref
- Shinkyu Jeong, Mitsuhiro Murayama, and Kazuomi Yamamoto. 2005. Efficient optimization design method using kriging model. Journal of aircraft 42, 2 (2005), 413--420.Google ScholarCross Ref
- Yaochu Jin. 2011. Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm and Evolutionary Computation 1, 2 (2011), 61--70.Google ScholarCross Ref
- Donald R Jones, Matthias Schonlau, and William J Welch. 1998. Efficient global optimization of expensive black-box functions. Journal of Global optimization 13, 4 (1998), 455--492. Google ScholarDigital Library
- Stefan Kern, Nikolaus Hansen, and Petros Koumoutsakos. 2006. Local metamodels for optimization using evolution strategies. In Parallel Problem Solving from Nature-PPSN IX. Springer, 939--948. Google ScholarDigital Library
- Pierric Kersaudy, Bruno Sudret, Nadège Varsier, Odile Picon, and Joe Wiart. 2015. A new surrogate modeling technique combining Kriging and polynomial chaos expansions-Application to uncertainty analysis in computational dosimetry. J. Comput. Phys. 286 (2015), 103--117. Google ScholarDigital Library
- Joshua Knowles, David Corne, and Alan Reynolds. 2009. Noisy multiobjective optimization on a budget of 250 evaluations. In International Conference on Evolutionary Multi-Criterion Optimization. Springer, 36--50. Google ScholarDigital Library
- Tipaluck Krityakierne, Taimoor Akhtar, and Christine A Shoemaker. 2016. SOP: parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems. Journal of Global Optimization 66, 3 (2016), 417--437. Google ScholarDigital Library
- Rémi Lam, Matthias Poloczek, Peter Frazier, and Karen E Willcox. 2018. Advances in bayesian optimization with applications in aerospace engineering. In 2018 AIAA Non-Deterministic Approaches Conference. 1656.Google ScholarCross Ref
- Rémi Lam, Matthias Poloczek, Peter Frazier, and Karen E. Willcox. 2018. Advances in Bayesian Optimization with Applications in Aerospace Engineering. In 2018 AIAA Non-Deterministic Approaches Conference.Google Scholar
- Christos Lataniotis, Stefano Marelli, and Bruno Sudret. 2018. Extending classical surrogate modelling to ultrahigh dimensional problems through supervised dimensionality reduction: a data-driven approach. arXiv preprint arXiv:1812.06309 (2018).Google Scholar
- Minh Nghia Le, Yew Soon Ong, Stefan Menzel, Yaochu Jin, and Bernhard Sendhoff. 2013. Evolution by adapting surrogates. Evolutionary computation 21, 2 (2013), 313--340.Google Scholar
- Rhea P. Liem, Gaetan K. W. Kenway, and Joaquim R. R. A. Martins. 2015. Multi-mission Aircraft Fuel Burn Minimization via Multipoint Aerostructural Optimization. AIAA Journal 53, 1 (2015), 104--122.Google ScholarCross Ref
- Rhea P. Liem, G. K. W. Kenway, and Joaquim R. R. A. Martins. 2017. Expected Drag Minimization for Aerodynamic Design Optimization Based on Aircraft Operational Data. Aerospace Science and Technology 63 (2017), 344--362.Google ScholarCross Ref
- Rhea P Liem, Charles A Mader, and Joaquim RRA Martins. 2015. Surrogate models and mixtures of experts in aerodynamic performance prediction for aircraft mission analysis. Aerospace Science and Technology 43 (2015), 126--151.Google ScholarCross Ref
- Bo Liu, Slawomir Koziel, and Qingfu Zhang. 2016. A multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization problems. Journal of computational science 12 (2016), 28--37.Google ScholarCross Ref
- Jun Liu, ZH Han, and WP Song. 2012. Comparison of infill sampling criteria in kriging-based aerodynamic optimization. In 28th Congress of the International Council of the Aeronautical Sciences. 23--28.Google Scholar
- Weiyu Liu and Stephen Batill. 2002. Gradient-enhanced response surface approximations using kriging models. In 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. 5456.Google ScholarCross Ref
- Cheng Lu, Yun-Wen Feng, Rhea P. Liem, and Cheng-Wei Fei. 2018. Improved Kriging with extremum response surface method for structural dynamic reliability and sensitivity analyses. Aerospace Science and Technology 76 (2018), 164--175.Google ScholarCross Ref
- Jiannan Luo and Wenxi Lu. 2014. Comparison of surrogate models with different methods in groundwater remediation process. Journal of Earth System Science 123, 7 (2014), 1579--1589.Google ScholarCross Ref
- Stefano Marelli and Bruno Sudret. 2018. An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis. Structural Safety 75 (2018), 67--74.Google ScholarCross Ref
- Ruben Martinez-Cantin, Kevin Tee, and Michael McCourt. 2017. Practical Bayesian optimization in the presence of outliers. arXiv preprint arXiv:1712.04567 (2017).Google Scholar
- Efrén Mezura-Montes and Carlos A Coello Coello. 2011. Constraint-handling in nature-inspired numerical optimization: past, present and future. Swarm and Evolutionary Computation 1, 4 (2011), 173--194.Google ScholarCross Ref
- Miha Mlakar, Dejan Petelin, Tea Tušar, and Bogdan Filipič. 2015. GP-DEMO: differential evolution for multiobjective optimization based on Gaussian process models. European Journal of Operational Research 243, 2 (2015), 347--361.Google ScholarCross Ref
- Maliki Moustapha, Bruno Sudret, Jean-Marc Bourinet, and Benoît Guillaume. 2016. Quantile-based optimization under uncertainties using adaptive Kriging surrogate models. Structural and multidisciplinary optimization 54, 6 (2016), 1403--1421. Google ScholarDigital Library
- Tanmoy Mukhopadhyay, S Chakraborty, S Dey, S Adhikari, and R Chowdhury. 2017. A critical assessment of Kriging model variants for high-fidelity uncertainty quantification in dynamics of composite shells. Archives of Computational Methods in Engineering 24, 3 (2017), 495--518.Google ScholarCross Ref
- Mahdi Arian Nik, Kazem Fayazbakhsh, Damiano Pasini, and Larry Lessard. 2012. Surrogate-based multi-objective optimization of a composite laminate with curvilinear fibers. Composite Structures 94, 8 (2012), 2306--2313.Google ScholarCross Ref
- Shigeru Obayashi, Shin-Kyu Jeong, Koji Shimoyama, Kazuhisa Chiba, and Hiroyuki Morino. 2010. Multi-objective design exploration and its applications. International Journal of Aeronautical and Space Sciences 11, 4 (2010), 247--265.Google ScholarCross Ref
- Yew-Soon Ong, Kai Yew Lum, and Prasanth B Nair. 2008. Hybrid evolutionary algorithm with Hermite radial basis function interpolants for computationally expensive adjoint solvers. Computational Optimization and Applications 39, 1 (2008), 97--119. Google ScholarDigital Library
- Ricardo M Paiva, André R D. Carvalho, Curran Crawford, and Afzal Suleman. 2010. Comparison of surrogate models in a multidisciplinary optimization framework for wing design. AIAA journal 48, 5 (2010), 995--1006.Google ScholarCross Ref
- Pramudita Satria Palar, Takeshi Tsuchiya, and Geoffrey Thomas Parks. 2016. Multi-fidelity non-intrusive polynomial chaos based on regression. Computer Methods in Applied Mechanics and Engineering 305 (2016), 579--606.Google ScholarCross Ref
- Pramudita Satria Palar, Kaifeng Yang, Koji Shimoyama, Michael Emmerich, and Thomas Bäck. 2018. Multi-objective aerodynamic design with user preference using truncated expected hypervolume improvement. In Proceedings of the Genetic and Evolutionary Computation Conference. ACM, 1333--1340. Google ScholarDigital Library
- Seongeon Park, Jonggeol Na, Minjun Kim, and Jong Min Lee. 2018. Multi-objective Bayesian optimization of chemical reactor design using computational fluid dynamics. Computers & Chemical Engineering 119 (2018), 25--37.Google ScholarCross Ref
- Ji Peng, Jerrad Hampton, and Alireza Doostan. 2016. On polynomial chaos expansion via gradient-enhanced âĎŞ1-minimization. J. Comput. Phys. 310 (2016), 440--458. Google ScholarDigital Library
- J Peter and M Marcelet. 2008. Comparison of surrogate models for turbomachinery design. Wseas transactions on fluid mechanics 3, 1 (2008), 10--17.Google Scholar
- Victor Picheny, Tobias Wagner, and David Ginsbourger. 2013. A benchmark of kriging-based infill criteria for noisy optimization. Structural and Multidisciplinary Optimization 48, 3 (2013), 607--626. Google ScholarDigital Library
- Louis B Rall. 1981. Automatic differentiation: Techniques and applications. (1981).Google Scholar
- Rommel G Regis. 2011. Stochastic radial basis function algorithms for large-scale optimization involving expensive black-box objective and constraint functions. Computers & Operations Research 38, 5 (2011), 837--853. Google ScholarDigital Library
- Rommel G Regis. 2014. Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points. Engineering Optimization 46, 2 (2014), 218--243.Google ScholarCross Ref
- Rommel G Regis. 2016. Multi-objective constrained black-box optimization using radial basis function surrogates. Journal of computational science 16 (2016), 140--155.Google ScholarCross Ref
- Rommel G Regis and Christine A Shoemaker. 2005. Constrained global optimization of expensive black box functions using radial basis functions. Journal of Global optimization 31, 1 (2005), 153--171. Google ScholarDigital Library
- Rommel G Regis and Christine A Shoemaker. 2007. A stochastic radial basis function method for the global optimization of expensive functions. INFORMS Journal on Computing 19, 4 (2007), 497--509. Google ScholarDigital Library
- Jose F Rodriguez, John E Renaud, and Layne T Watson. 1998. Trust region augmented Lagrangian methods for sequential response surface approximation and optimization. Journal of mechanical design 120, 1 (1998), 58--66.Google ScholarCross Ref
- Markus P Rumpfkeil. 2012. Optimizations under uncertainty using gradients, Hessians, and surrogate models. AIAA journal 51, 2 (2012), 444--451.Google ScholarCross Ref
- Jerome Sacks, William J Welch, Toby J Mitchell, and Henry P Wynn. 1989. Design and analysis of computer experiments. Statistical science (1989), 409--423.Google Scholar
- Abdus Samad, Kwang-Yong Kim, Tushar Goel, Raphael T Haftka, and Wei Shyy. 2008. Multiple surrogate modeling for axial compressor blade shape optimization. Journal of Propulsion and Power 24, 2 (2008), 301--310.Google ScholarCross Ref
- Roland Schöbi, Bruno Sudret, and Stefano Marelli. 2016. Rare event estimation using polynomial-chaos kriging. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 3, 2 (2016), D4016002.Google Scholar
- Matthias Schonlau, William J Welch, and Donald R Jones. 1998. Global versus local search in constrained optimization of computer models. Lecture Notes-Monograph Series (1998), 11--25.Google Scholar
- Warren Scott, Peter Frazier, and Warren Powell. 2011. The correlated knowledge gradient for simulation optimization of continuous parameters using gaussian process regression. SIAM Journal on Optimization 21, 3 (2011), 996--1026.Google ScholarCross Ref
- Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, and Nando de Freitas. 2016. Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proc. IEEE 104, 1 (January 2016), 148--175.Google ScholarCross Ref
- Koji Shimoyama, Soshi Kawai, and Juan J Alonso. 2013. Dynamic adaptive sampling based on Kriging surrogate models for efficient uncertainty quantification. In 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. 1470.Google Scholar
- Prashant Singh, Ivo Couckuyt, Khairy Elsayed, Dirk Deschrijver, and Tom Dhaene. 2016. Shape optimization of a cyclone separator using multi-objective surrogate-based optimization. Applied Mathematical Modelling 40, 5--6 (2016), 4248--4259.Google ScholarCross Ref
- Ilya M Sobol. 2001. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and computers in simulation 55, 1--3 (2001), 271--280. Google ScholarDigital Library
- Jian Song, Yun Yang, Jianfeng Wu, Jichun Wu, Xiaomin Sun, and Jin Lin. 2018. Adaptive surrogate model based multiobjective optimization for coastal aquifer management. Journal of hydrology 561 (2018), 98--111.Google ScholarCross Ref
- Lu-Kai Song, Guang-Chen Bai, Cheng-Wei Fei, and Rhea P. Liem. 2018. Transient probabilistic design of a flexible multibody system using a dynamic fuzzy neural network method with distributed collaborative strategy. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering. (2018).Google Scholar
- Bruno Sudret. 2008. Global sensitivity analysis using polynomial chaos expansions. Reliability engineering & system safety 93, 7 (2008), 964--979.Google Scholar
- Chaoli Sun, Yaochu Jin, Jianchao Zeng, and Yang Yu. 2015. A two-layer surrogate-assisted particle swarm optimization algorithm. Soft computing 19, 6 (2015), 1461--1475. Google ScholarDigital Library
- David JJ Toal. 2015. Some considerations regarding the use of multi-fidelity Kriging in the construction of surrogate models. Structural and Multidisciplinary Optimization 51, 6 (2015), 1223--1245. Google ScholarDigital Library
- Rohit Tripathy, Ilias Bilionis, and Marcial Gonzalez. 2016. Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation. J. Comput. Phys. 321 (2016), 191--223. Google ScholarDigital Library
- Stef Van Buuren. 2018. Flexible imputation of missing data. Chapman and Hall/CRC.45Google Scholar
- Felipe AC Viana, Raphael T Haftka, and Layne T Watson. 2013. Efficient global optimization algorithm assisted by multiple surrogate techniques. Journal of Global Optimization 56, 2 (2013), 669--689. Google ScholarDigital Library
- Felipe AC Viana, Timothy W Simpson, Vladimir Balabanov, and Vasilli Toropov. 2014. Special section on multidisciplinary design optimization: metamodeling in multidisciplinary design optimization: how far have we really come? AIAA journal 52, 4 (2014), 670--690.Google ScholarCross Ref
- Hao Wang, Bas van Stein, Michael Emmerich, and Thomas Bäck. 2017. Time complexity reduction in efficient global optimization using cluster kriging. In Proceedings of the Genetic and Evolutionary Computation Conference. ACM, 889--896. Google ScholarDigital Library
- Dongbin Xiu and George Em Karniadakis. 2002. The Wiener-Askey polynomial chaos for stochastic differential equations. SIAM journal on scientific computing 24, 2 (2002), 619--644. Google ScholarDigital Library
- Wataru Yamazaki, Markus Rumpfkeil, and Dimitri Mavriplis. 2010. Design optimization utilizing Gradient/Hessian enhanced surrogate model. In 28th AIAA Applied Aerodynamics Conference. 43--63.Google ScholarCross Ref
- Jefry Yanto and Rhea P. Liem. 2018. Aircraft fuel burn performance study: a data-enhanced modeling approach. Transportation Research Part D: Transport and Environment 65 (2018), 574--595.Google ScholarCross Ref
- Dawei Zhan, Jiachang Qian, and Yuansheng Cheng. 2017. Balancing global and local search in parallel efficient global optimization algorithms. Journal of Global Optimizatio 67, 4 (2017), 873--892. Google ScholarDigital Library
- Ke-Shi Zhang, Zhong-Hua Han, Zhong-Jian Gao, and Yuan Wang. 2019. Constraint aggregation for large number of constraints in wing surrogate-based optimization. Structural and Multidisciplinary Optimization 59, 2 (2019), 421--438. Google ScholarDigital Library
- Qingfu Zhang, Wudong Liu, Edward Tsang, and Botond Virginas. 2010. Expensive multiobjective optimization by MOEA/D with Gaussian process model. IEEE Transactions on Evolutionary Computation 14, 3 (2010), 456--474. Google ScholarDigital Library
- Lavi R Zuhal, Cahya Amalinadhi, Yohanes B Dwianto, Pramudita S Palar, and Koji Shimoyama. 2018. Benchmarking Multi-Objective Bayesian Global Optimization Strategies for Aerodynamic Design. In 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. 0914.Google Scholar
Index Terms
- On the use of surrogate models in engineering design optimization and exploration: the key issues
Recommendations
Blackbox optimization and surrogate models for machining free-form surfaces
AbstractThis paper introduces an optimization model for machining free-form surfaces. It involves one categorical decision variable and continuous decision variables. Its objective function is partially separable. It is composed of two blackboxes: a ...
Highlights- A new partitioning-and-machining optimization model is introduced.
- The optimization problem is solved using the blackbox optimization software NOMAD.
- To enhance the optimization process, four static surrogates are proposed.
- ...
Sequential Hybridization of Online Surrogate Models for Continuous Optimization
GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationSurrogate-based optimization is a common practice in different application fields in engineering and sciences, mainly when the objective function is computationally expensive. The selection of the best modeling approach to design the surrogate impacts ...
Automatic selection for general surrogate models
In design engineering problems, the use of surrogate models (also called metamodels) instead of expensive simulations have become very popular. Surrogate models include individual models (regression, kriging, neural network...) or a combination of ...
Comments