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Model-Based Stochastic Search Methods

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Handbook of Simulation Optimization

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 216))

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.

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Acknowledgements

This work was supported in part by the National Science Foundation (NSF) under Grant CMMI-1130761 and by the Air Force Office of Scientific Research (AFOSR) under Grant FA95501010340.

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Authors and Affiliations

  1. Stony Brook University, Stony Brook, NY, USA

    Jiaqiao Hu

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  1. Jiaqiao Hu
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Correspondence to Jiaqiao Hu .

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Editors and Affiliations

  1. University of Maryland, College Park, Maryland, USA

    Michael C Fu

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Hu, J. (2015). Model-Based Stochastic Search Methods. In: Fu, M. (eds) Handbook of Simulation Optimization. International Series in Operations Research & Management Science, vol 216. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1384-8_12

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