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Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models

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Cited by:

  1. Julien Marzat & Eric Walter & Hélène Piet-Lahanier, 2013. "Worst-case global optimization of black-box functions through Kriging and relaxation," Journal of Global Optimization, Springer, vol. 55(4), pages 707-727, April.
  2. Ehsan Mehdad & Jack P. C. Kleijnen, 2018. "Efficient global optimisation for black-box simulation via sequential intrinsic Kriging," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(11), pages 1725-1737, November.
  3. Gyoung-Ja Lee & Won-Ju Hwang & Jin-Ju Park & Min-Ku Lee, 2019. "Study of Sensitive Parameters on the Sensor Performance of a Compression-Type Piezoelectric Accelerometer Based on the Meta-Model," Energies, MDPI, vol. 12(7), pages 1-11, April.
  4. Zheng, Liang & Xue, Xinfeng & Xu, Chengcheng & Ran, Bin, 2019. "A stochastic simulation-based optimization method for equitable and efficient network-wide signal timing under uncertainties," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 287-308.
  5. Enlu Zhou & Shalabh Bhatnagar, 2018. "Gradient-Based Adaptive Stochastic Search for Simulation Optimization Over Continuous Space," INFORMS Journal on Computing, INFORMS, vol. 30(1), pages 154-167, February.
  6. Mehdad, E. & Kleijnen, Jack P.C., 2014. "Global Optimization for Black-box Simulation via Sequential Intrinsic Kriging," Discussion Paper 2014-063, Tilburg University, Center for Economic Research.
  7. Komarudin & Tim De Feyter & Marie-Anne Guerry & Greet Vanden Berghe, 2020. "The extended roster quality staffing problem: addressing roster quality variation within a staffing planning period," Journal of Scheduling, Springer, vol. 23(2), pages 253-264, April.
  8. Eldred, M.S. & Swiler, L.P. & Tang, G., 2011. "Mixed aleatory-epistemic uncertainty quantification with stochastic expansions and optimization-based interval estimation," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1092-1113.
  9. Hau T. Mai & Jaewook Lee & Joowon Kang & H. Nguyen-Xuan & Jaehong Lee, 2022. "An Improved Blind Kriging Surrogate Model for Design Optimization Problems," Mathematics, MDPI, vol. 10(16), pages 1-19, August.
  10. Swetlana Herbrandt & Uwe Ligges & Manuel Pinho Ferreira & Michael Kansteiner & Dirk Biermann & Wolfgang Tillmann & Claus Weihs, 2018. "Model based optimization of a statistical simulation model for single diamond grinding," Computational Statistics, Springer, vol. 33(3), pages 1127-1143, September.
  11. Majed Hadid & Adel Elomri & Regina Padmanabhan & Laoucine Kerbache & Oualid Jouini & Abdelfatteh El Omri & Amir Nounou & Anas Hamad, 2022. "Clustering and Stochastic Simulation Optimization for Outpatient Chemotherapy Appointment Planning and Scheduling," IJERPH, MDPI, vol. 19(23), pages 1-34, November.
  12. Diana M. Negoescu & Peter I. Frazier & Warren B. Powell, 2011. "The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery," INFORMS Journal on Computing, INFORMS, vol. 23(3), pages 346-363, August.
  13. Zhe Zhou & Fusheng Bai, 2018. "An adaptive framework for costly black-box global optimization based on radial basis function interpolation," Journal of Global Optimization, Springer, vol. 70(4), pages 757-781, April.
  14. Songhao Wang & Szu Hui Ng & William Benjamin Haskell, 2022. "A Multilevel Simulation Optimization Approach for Quantile Functions," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 569-585, January.
  15. Dawei Zhan & Jiachang Qian & Yuansheng Cheng, 2017. "Balancing global and local search in parallel efficient global optimization algorithms," Journal of Global Optimization, Springer, vol. 67(4), pages 873-892, April.
  16. Jialei Wang & Scott C. Clark & Eric Liu & Peter I. Frazier, 2020. "Parallel Bayesian Global Optimization of Expensive Functions," Operations Research, INFORMS, vol. 68(6), pages 1850-1865, November.
  17. Diariétou Sambakhé & Lauriane Rouan & Jean-Noël Bacro & Eric Gozé, 2019. "Conditional optimization of a noisy function using a kriging metamodel," Journal of Global Optimization, Springer, vol. 73(3), pages 615-636, March.
  18. Tipaluck Krityakierne & Taimoor Akhtar & Christine A. Shoemaker, 2016. "SOP: parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems," Journal of Global Optimization, Springer, vol. 66(3), pages 417-437, November.
  19. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
  20. Kleijnen, J.P.C. & van Beers, W.C.M. & van Nieuwenhuyse, I., 2008. "Constrained Optimization in Simulation : A Novel Approach," Discussion Paper 2008-95, Tilburg University, Center for Economic Research.
  21. Janis Janusevskis & Rodolphe Le Riche, 2013. "Simultaneous kriging-based estimation and optimization of mean response," Journal of Global Optimization, Springer, vol. 55(2), pages 313-336, February.
  22. Shin-Fu Tsai & Chih-Chien Shen & Chen-Tuo Liao, 2021. "Bayesian Optimization Approaches for Identifying the Best Genotype from a Candidate Population," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 519-537, December.
  23. Dawei Zhan & Huanlai Xing, 2020. "Expected improvement for expensive optimization: a review," Journal of Global Optimization, Springer, vol. 78(3), pages 507-544, November.
  24. Peter Frazier & Warren Powell & Savas Dayanik, 2009. "The Knowledge-Gradient Policy for Correlated Normal Beliefs," INFORMS Journal on Computing, INFORMS, vol. 21(4), pages 599-613, November.
  25. Jalali, Hamed & Van Nieuwenhuyse, Inneke & Picheny, Victor, 2017. "Comparison of Kriging-based algorithms for simulation optimization with heterogeneous noise," European Journal of Operational Research, Elsevier, vol. 261(1), pages 279-301.
  26. Felipe Viana & Raphael Haftka & Layne Watson, 2013. "Efficient global optimization algorithm assisted by multiple surrogate techniques," Journal of Global Optimization, Springer, vol. 56(2), pages 669-689, June.
  27. Bolong Cheng & Arta Jamshidi & Warren Powell, 2015. "Optimal learning with a local parametric belief model," Journal of Global Optimization, Springer, vol. 63(2), pages 401-425, October.
  28. Taimoor Akhtar & Christine Shoemaker, 2016. "Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection," Journal of Global Optimization, Springer, vol. 64(1), pages 17-32, January.
  29. Mickaël Binois & David Ginsbourger & Olivier Roustant, 2020. "On the choice of the low-dimensional domain for global optimization via random embeddings," Journal of Global Optimization, Springer, vol. 76(1), pages 69-90, January.
  30. Emre Barut & Warren Powell, 2014. "Optimal learning for sequential sampling with non-parametric beliefs," Journal of Global Optimization, Springer, vol. 58(3), pages 517-543, March.
  31. Huashuai Qu & Ilya O. Ryzhov & Michael C. Fu & Zi Ding, 2015. "Sequential Selection with Unknown Correlation Structures," Operations Research, INFORMS, vol. 63(4), pages 931-948, August.
  32. Yuan, Jun & Shi, Xunpeng & He, Junliang, 2024. "LNG market liberalization and LNG transportation: Evaluation based on fleet size and composition model," Applied Energy, Elsevier, vol. 358(C).
  33. Tansu Alpcan, 2013. "A framework for optimization under limited information," Journal of Global Optimization, Springer, vol. 55(3), pages 681-706, March.
  34. Rommel Regis & Christine Shoemaker, 2013. "A quasi-multistart framework for global optimization of expensive functions using response surface models," Journal of Global Optimization, Springer, vol. 56(4), pages 1719-1753, August.
  35. Song, Jingwen & Cui, Yifan & Wei, Pengfei & Valdebenito, Marcos A. & Zhang, Weihong, 2024. "Constrained Bayesian optimization algorithms for estimating design points in structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
  36. Kleijnen, Jack P.C. & Mehdad, E., 2013. "Conditional simulation for efficient global optimization," Other publications TiSEM 52e4860d-9887-4a63-b19a-7, Tilburg University, School of Economics and Management.
  37. Nestor Queipo & Salvador Pintos & Efrain Nava, 2013. "Setting targets for surrogate-based optimization," Journal of Global Optimization, Springer, vol. 55(4), pages 857-875, April.
  38. Dawei Zhan & Jiachang Qian & Yuansheng Cheng, 2017. "Pseudo expected improvement criterion for parallel EGO algorithm," Journal of Global Optimization, Springer, vol. 68(3), pages 641-662, July.
  39. M Laguna & J Molina & F Pérez & R Caballero & A G Hernández-Díaz, 2010. "The challenge of optimizing expensive black boxes: a scatter search/rough set theory approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 53-67, January.
  40. Peter L. Salemi & Eunhye Song & Barry L. Nelson & Jeremy Staum, 2019. "Gaussian Markov Random Fields for Discrete Optimization via Simulation: Framework and Algorithms," Operations Research, INFORMS, vol. 67(1), pages 250-266, January.
  41. Qi Fan & Jiaqiao Hu, 2018. "Surrogate-Based Promising Area Search for Lipschitz Continuous Simulation Optimization," INFORMS Journal on Computing, INFORMS, vol. 30(4), pages 677-693, November.
  42. Shande Li & Shuai Yuan & Shaowei Liu & Jian Wen & Qibai Huang, 2022. "Research on an Accuracy Optimization Algorithm of Kriging Model Based on a Multipoint Filling Criterion," Mathematics, MDPI, vol. 10(9), pages 1-11, May.
  43. Donghun Lee, 2022. "Knowledge Gradient: Capturing Value of Information in Iterative Decisions under Uncertainty," Mathematics, MDPI, vol. 10(23), pages 1-20, November.
  44. Qun Meng & Songhao Wang & Szu Hui Ng, 2022. "Combined Global and Local Search for Optimization with Gaussian Process Models," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 622-637, January.
  45. Daniel Lizotte & Russell Greiner & Dale Schuurmans, 2012. "An experimental methodology for response surface optimization methods," Journal of Global Optimization, Springer, vol. 53(4), pages 699-736, August.
  46. Pedrielli, Giulia & Wang, Songhao & Ng, Szu Hui, 2020. "An extended Two-Stage Sequential Optimization approach: Properties and performance," European Journal of Operational Research, Elsevier, vol. 287(3), pages 929-945.
  47. Picheny, Victor & Ginsbourger, David, 2014. "Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1035-1053.
  48. Rojas Gonzalez, Sebastian & Jalali, Hamed & Van Nieuwenhuyse, Inneke, 2020. "A multiobjective stochastic simulation optimization algorithm," European Journal of Operational Research, Elsevier, vol. 284(1), pages 212-226.
  49. Rivera-Gómez, Héctor & Gharbi, Ali & Kenné, Jean Pierre, 2013. "Joint production and major maintenance planning policy of a manufacturing system with deteriorating quality," International Journal of Production Economics, Elsevier, vol. 146(2), pages 575-587.
  50. Dellino, G. & Lino, P. & Meloni, C. & Rizzo, A., 2009. "Kriging metamodel management in the design optimization of a CNG injection system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2345-2360.
  51. Svenson, Joshua & Santner, Thomas, 2016. "Multiobjective optimization of expensive-to-evaluate deterministic computer simulator models," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 250-264.
  52. Mark D. Rodgers & David W. Coit & Frank A. Felder & Annmarie G. Carlton, 2019. "A Metamodeling Framework for Quantifying Health Damages of Power Grid Expansion Plans," IJERPH, MDPI, vol. 16(10), pages 1-21, May.
  53. Zilong Wang & Marianthi Ierapetritou, 2018. "Surrogate-based feasibility analysis for black-box stochastic simulations with heteroscedastic noise," Journal of Global Optimization, Springer, vol. 71(4), pages 957-985, August.
  54. Jing Xie & Peter I. Frazier & Stephen E. Chick, 2016. "Bayesian Optimization via Simulation with Pairwise Sampling and Correlated Prior Beliefs," Operations Research, INFORMS, vol. 64(2), pages 542-559, April.
  55. Satyajith Amaran & Nikolaos V. Sahinidis & Bikram Sharda & Scott J. Bury, 2016. "Simulation optimization: a review of algorithms and applications," Annals of Operations Research, Springer, vol. 240(1), pages 351-380, May.
  56. Candelieri Antonio, 2021. "Sequential model based optimization of partially defined functions under unknown constraints," Journal of Global Optimization, Springer, vol. 79(2), pages 281-303, February.
  57. Kleijnen, Jack P.C. & Beers, Wim van & Nieuwenhuyse, Inneke van, 2010. "Constrained optimization in expensive simulation: Novel approach," European Journal of Operational Research, Elsevier, vol. 202(1), pages 164-174, April.
  58. Shengguan Xu & Hongquan Chen, 2018. "Nash game based efficient global optimization for large-scale design problems," Journal of Global Optimization, Springer, vol. 71(2), pages 361-381, June.
  59. Fu, Quanlu & Wu, Jiyan & Wu, Xuemian & Sun, Jian & Tian, Ye, 2024. "Managing network congestion with link-based incentives: A surrogate-based optimization approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 182(C).
  60. Raymond K. W. Wong & Curtis B. Storlie & Thomas C. M. Lee, 2017. "A frequentist approach to computer model calibration," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 635-648, March.
  61. Xiqun (Michael) Chen & Xiang He & Chenfeng Xiong & Zheng Zhu & Lei Zhang, 2019. "A Bayesian Stochastic Kriging Optimization Model Dealing with Heteroscedastic Simulation Noise for Freeway Traffic Management," Transportation Science, INFORMS, vol. 53(2), pages 545-565, March.
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