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A Model Reference Adaptive Search Method for Global Optimization

Author

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  • Jiaqiao Hu

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

  • Michael C. Fu

    (Robert H. Smith School of Business, and Institute for Systems Research, University of Maryland, College Park, Maryland 20742)

  • Steven I. Marcus

    (Department of Electrical and Computer Engineering, and Institute for Systems Research, University of Maryland, College Park, Maryland 20742)

Abstract

Model reference adaptive search (MRAS) for solving global optimization problems works with a parameterized probabilistic model on the solution space and generates at each iteration a group of candidate solutions. These candidate solutions are then used to update the parameters associated with the probabilistic model in such a way that the future search will be biased toward the region containing high-quality solutions. The parameter updating procedure in MRAS is guided by a sequence of implicit probabilistic models we call reference models. We provide a particular algorithm instantiation of the MRAS method, where the sequence of reference models can be viewed as the generalized probability distribution models for estimation of distribution algorithms (EDAs) with proportional selection scheme. In addition, we show that the model reference framework can also be used to describe the recently proposed cross-entropy (CE) method for optimization and to study its properties. Hence, this paper can also be seen as a study on the effectiveness of combining CE and EDAs. We prove global convergence of the proposed algorithm in both continuous and combinatorial domains, and we carry out numerical studies to illustrate the performance of the algorithm.

Suggested Citation

  • Jiaqiao Hu & Michael C. Fu & Steven I. Marcus, 2007. "A Model Reference Adaptive Search Method for Global Optimization," Operations Research, INFORMS, vol. 55(3), pages 549-568, June.
    • Handle: RePEc:inm:oropre:v:55:y:2007:i:3:p:549-568
    DOI: 10.1287/opre.1060.0367
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    References listed on IDEAS

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    Cited by:

    1. Xi Chen & Enlu Zhou, 2015. "Population model-based optimization," Journal of Global Optimization, Springer, vol. 63(1), pages 125-148, September.
    2. Kun Lin & Steven I. Marcus, 2016. "Cumulative weighting optimization," Journal of Global Optimization, Springer, vol. 65(3), pages 487-512, July.
    3. Joshua Q. Hale & Enlu Zhou & Jiming Peng, 2017. "A Lagrangian search method for the P-median problem," Journal of Global Optimization, Springer, vol. 69(1), pages 137-156, September.
    4. Qi Zhang & Jiaqiao Hu, 2019. "Simulation Optimization Using Multi-Time-Scale Adaptive Random Search," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(06), pages 1-34, December.
    5. Lan, Yingjie & Ball, Michael O. & Karaesmen, Itir Z. & Zhang, Jean X. & Liu, Gloria X., 2015. "Analysis of seat allocation and overbooking decisions with hybrid information," European Journal of Operational Research, Elsevier, vol. 240(2), pages 493-504.
    6. Trond Steihaug & Sara Suleiman, 2013. "Global convergence and the Powell singular function," Journal of Global Optimization, Springer, vol. 56(3), pages 845-853, July.
    7. 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.
    8. Kabirian, Alireza & Ólafsson, Sigurdur, 2011. "Continuous optimization via simulation using Golden Region search," European Journal of Operational Research, Elsevier, vol. 208(1), pages 19-27, January.
    9. Jiaqiao Hu & Hyeong Chang & Michael Fu & Steven Marcus, 2011. "Dynamic sample budget allocation in model-based optimization," Journal of Global Optimization, Springer, vol. 50(4), pages 575-596, August.
    10. Shi Pu & Alfredo Garcia, 2018. "A Flocking-Based Approach for Distributed Stochastic Optimization," Operations Research, INFORMS, vol. 66(1), pages 267-281, January.
    11. Joshua Q. Hale & Helin Zhu & Enlu Zhou, 2020. "Domination Measure: A New Metric for Solving Multiobjective Optimization," INFORMS Journal on Computing, INFORMS, vol. 32(3), pages 565-581, July.
    12. Johannes Royset, 2013. "On sample size control in sample average approximations for solving smooth stochastic programs," Computational Optimization and Applications, Springer, vol. 55(2), pages 265-309, June.
    13. Liujia Hu & Sigrún Andradóttir, 2019. "An Asymptotically Optimal Set Approach for Simulation Optimization," INFORMS Journal on Computing, INFORMS, vol. 31(1), pages 21-39, February.
    14. Jie Xu & Barry L. Nelson & L. Jeff Hong, 2013. "An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems," INFORMS Journal on Computing, INFORMS, vol. 25(1), pages 133-146, February.
    15. 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.
    16. Fan, Qi & Tan, Ken Seng & Zhang, Jinggong, 2023. "Empirical tail risk management with model-based annealing random search," Insurance: Mathematics and Economics, Elsevier, vol. 110(C), pages 106-124.
    17. Andrea Cassioli & Fabio Schoen, 2013. "Global optimization of expensive black box problems with a known lower bound," Journal of Global Optimization, Springer, vol. 57(1), pages 177-190, September.
    18. Yue Sun & Alfredo Garcia, 2017. "Interactive model-based search with reactive resource allocation," Journal of Global Optimization, Springer, vol. 67(1), pages 135-149, January.
    19. Scott, Alexandre & Metzler, Adam, 2015. "A general importance sampling algorithm for estimating portfolio loss probabilities in linear factor models," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 279-293.
    20. Helin Zhu & Joshua Hale & Enlu Zhou, 2018. "Simulation optimization of risk measures with adaptive risk levels," Journal of Global Optimization, Springer, vol. 70(4), pages 783-809, April.
    21. Heath, Jeffrey W. & Fu, Michael C. & Jank, Wolfgang, 2009. "New global optimization algorithms for model-based clustering," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 3999-4017, October.
    22. Zheng Peng & Donghua Wu & Quan Zheng, 2013. "A Level-Value Estimation Method and Stochastic Implementation for Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 156(2), pages 493-523, February.
    23. Zheng Peng & Donghua Wu & Wenxing Zhu, 2016. "The robust constant and its applications in random global search for unconstrained global optimization," Journal of Global Optimization, Springer, vol. 64(3), pages 469-482, March.

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