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Surrogate-Based Promising Area Search for Lipschitz Continuous Simulation Optimization

Author

Listed:
  • Qi Fan

    (Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, New York 11794)

  • Jiaqiao Hu

    (Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, New York 11794)

Abstract

We propose an adaptive search algorithm for solving simulation optimization problems with Lipschitz continuous objective functions. The method combines the strength of several popular strategies in simulation optimization. It employs the shrinking ball method to estimate the performance of sampled solutions and uses the performance estimates to fit a surrogate model that iteratively approximates the response surface of the objective function. The search for improved solutions at each iteration is then based on sampling from a promising region (a subset of the decision space) adaptively constructed to contain the point that optimizes the surrogate model. Under appropriate conditions, we show that the algorithm converges to the set of local optimal solutions with probability one. A computational study is also carried out to illustrate the algorithm and to compare its performance with some of the existing procedures.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orijoc:v:30:y:2018:i:4:p:677-693
    DOI: 10.1287/ijoc.2017.0801
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    References listed on IDEAS

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    Cited by:

    1. Zheng, Liang & Bao, Ji & Xu, Chengcheng & Tan, Zhen, 2022. "Biobjective robust simulation-based optimization for unconstrained problems," European Journal of Operational Research, Elsevier, vol. 299(1), pages 249-262.

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