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Model-Based Stochastic Search Methods

In: Handbook of Simulation Optimization

Author

Listed:
  • Jiaqiao Hu

    (Stony Brook University)

Abstract

Model-based algorithms are a class of stochastic search methods that have successfully addressed some hard deterministic optimization problems. However, their application to simulation optimization is relatively undeveloped. This chapter reviews the basic structure of model-based algorithms, describes some recently developed frameworks and approaches to the design and analysis of a class of model-based algorithms, and discusses their extensions to simulation optimization.

Suggested Citation

  • Jiaqiao Hu, 2015. "Model-Based Stochastic Search Methods," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 319-340, Springer.
    • Handle: RePEc:spr:isochp:978-1-4939-1384-8_12
    DOI: 10.1007/978-1-4939-1384-8_12
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    Cited by:

    1. Eric Larsen & Sébastien Lachapelle & Yoshua Bengio & Emma Frejinger & Simon Lacoste-Julien & Andrea Lodi, 2022. "Predicting Tactical Solutions to Operational Planning Problems Under Imperfect Information," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 227-242, January.
    2. 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.
    3. Shi Pu & Alfredo Garcia, 2018. "A Flocking-Based Approach for Distributed Stochastic Optimization," Operations Research, INFORMS, vol. 66(1), pages 267-281, January.

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