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Efficient space-filling and non-collapsing sequential design strategies for simulation-based modeling

Author

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  • Crombecq, K.
  • Laermans, E.
  • Dhaene, T.
Abstract
Simulated computer experiments have become a viable cost-effective alternative for controlled real-life experiments. However, the simulation of complex systems with multiple input and output parameters can be a very time-consuming process. Many of these high-fidelity simulators need minutes, hours or even days to perform one simulation. The goal of global surrogate modeling is to create an approximation model that mimics the original simulator, based on a limited number of expensive simulations, but can be evaluated much faster. The set of simulations performed to create this model is called the experimental design. Traditionally, one-shot designs such as the Latin hypercube and factorial design are used, and all simulations are performed before the first model is built. In order to reduce the number of simulations needed to achieve the desired accuracy, sequential design methods can be employed. These methods generate the samples for the experimental design one by one, without knowing the total number of samples in advance. In this paper, the authors perform an extensive study of new and state-of-the-art space-filling sequential design methods. It is shown that the new sequential methods proposed in this paper produce results comparable to the best one-shot experimental designs available right now.

Suggested Citation

  • Crombecq, K. & Laermans, E. & Dhaene, T., 2011. "Efficient space-filling and non-collapsing sequential design strategies for simulation-based modeling," European Journal of Operational Research, Elsevier, vol. 214(3), pages 683-696, November.
    • Handle: RePEc:eee:ejores:v:214:y:2011:i:3:p:683-696
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    References listed on IDEAS

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    1. Peter Z. G. Qian, 2009. "Nested Latin hypercube designs," Biometrika, Biometrika Trust, vol. 96(4), pages 957-970.
    2. Husslage, B.G.M., 2006. "Maximin designs for computer experiments," Other publications TiSEM 216147a0-9c82-48d5-8913-6, Tilburg University, School of Economics and Management.
    3. Batmaz, Inci & Tunali, Semra, 2003. "Small response surface designs for metamodel estimation," European Journal of Operational Research, Elsevier, vol. 145(2), pages 455-470, March.
    4. Grosso, A. & Jamali, A.R.M.J.U. & Locatelli, M., 2009. "Finding maximin latin hypercube designs by Iterated Local Search heuristics," European Journal of Operational Research, Elsevier, vol. 197(2), pages 541-547, September.
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    Citations

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    Cited by:

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    2. Koziel, Slawomir & Pietrenko-Dabrowska, Anna, 2022. "Constrained multi-objective optimization of compact microwave circuits by design triangulation and pareto front interpolation," European Journal of Operational Research, Elsevier, vol. 299(1), pages 302-312.
    3. Xiongxiong You & Mengya Zhang & Diyin Tang & Zhanwen Niu, 2022. "An active learning method combining adaptive kriging and weighted penalty for structural reliability analysis," Journal of Risk and Reliability, , vol. 236(1), pages 160-172, February.
    4. Kleijnen, Jack P.C., 2017. "Regression and Kriging metamodels with their experimental designs in simulation: A review," European Journal of Operational Research, Elsevier, vol. 256(1), pages 1-16.
    5. Fajemisin, Adejuyigbe O. & Maragno, Donato & den Hertog, Dick, 2024. "Optimization with constraint learning: A framework and survey," European Journal of Operational Research, Elsevier, vol. 314(1), pages 1-14.
    6. Nicholas Davey & Nicolas Langrené & Wen Chen & Jonathan R. Rhodes & Simon Dunstall & Saman Halgamuge, 2023. "Designing higher value roads to preserve species at risk by optimally controlling traffic flow," Annals of Operations Research, Springer, vol. 320(2), pages 663-693, January.
    7. Vinícius Resende Domingues & Luan Carlos de Sena Monteiro Ozelim & André Pacheco de Assis & André Luís Brasil Cavalcante, 2022. "Combining Numerical Simulations, Artificial Intelligence and Intelligent Sampling Algorithms to Build Surrogate Models and Calculate the Probability of Failure of Urban Tunnels," Sustainability, MDPI, vol. 14(11), pages 1-29, May.
    8. Roy, Pamphile T. & Jofre, Lluís & Jouhaud, Jean-Christophe & Cuenot, Bénédicte, 2020. "Versatile sequential sampling algorithm using Kernel Density Estimation," European Journal of Operational Research, Elsevier, vol. 284(1), pages 201-211.

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