Gaussian mixture model-based random search for continuous optimization via simulation

Wenjie Sun; Zhaolin Hu; L. Jeff Hong

doi:10.1109/WSC.2018.8632380

Gaussian mixture model-based random search for continuous optimization via simulation

Wenjie Sun, Zhaolin Hu, L. Jeff Hong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

This paper studies integrated random search algorithms for continuous optimization-via-simulation (COvS) problems. We first tailor the Gaussian process-based search (GPS) algorithm to handle COvS problems. We then analyze the potential sampling issue of the GPS algorithm and propose to construct a desirable Gaussian mixture model (GMM) which is amenable for efficient sampling and at the same time also maintains the desirable property of exploitation and exploration trade-off. Then, we propose a Gaussian mixture model-based random search (GMRS) algorithm. We build global convergence of both the tailored GPS algorithm and the GMRS algorithm for COvS problems. Finally, we carry out some numerical studies to illustrate the performance of the GMRS algorithm.

Original language	English (US)
Title of host publication	WSC 2018 - 2018 Winter Simulation Conference
Subtitle of host publication	Simulation for a Noble Cause
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2003-2014
Number of pages	12
ISBN (Electronic)	9781538665725
DOIs	https://doi.org/10.1109/WSC.2018.8632380
State	Published - Jul 2 2018
Externally published	Yes
Event	2018 Winter Simulation Conference, WSC 2018 - Gothenburg, Sweden Duration: Dec 9 2018 → Dec 12 2018

Publication series

Name	Proceedings - Winter Simulation Conference
Volume	2018-December
ISSN (Print)	0891-7736

Conference

Conference	2018 Winter Simulation Conference, WSC 2018
Country/Territory	Sweden
City	Gothenburg
Period	12/9/18 → 12/12/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE

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10.1109/WSC.2018.8632380

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Sun, W., Hu, Z., & Jeff Hong, L. (2018). Gaussian mixture model-based random search for continuous optimization via simulation. In WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause (pp. 2003-2014). Article 8632380 (Proceedings - Winter Simulation Conference; Vol. 2018-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WSC.2018.8632380

Gaussian mixture model-based random search for continuous optimization via simulation. / Sun, Wenjie; Hu, Zhaolin; Jeff Hong, L.
WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause. Institute of Electrical and Electronics Engineers Inc., 2018. p. 2003-2014 8632380 (Proceedings - Winter Simulation Conference; Vol. 2018-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sun, W, Hu, Z & Jeff Hong, L 2018, Gaussian mixture model-based random search for continuous optimization via simulation. in WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause., 8632380, Proceedings - Winter Simulation Conference, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., pp. 2003-2014, 2018 Winter Simulation Conference, WSC 2018, Gothenburg, Sweden, 12/9/18. https://doi.org/10.1109/WSC.2018.8632380

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abstract = "This paper studies integrated random search algorithms for continuous optimization-via-simulation (COvS) problems. We first tailor the Gaussian process-based search (GPS) algorithm to handle COvS problems. We then analyze the potential sampling issue of the GPS algorithm and propose to construct a desirable Gaussian mixture model (GMM) which is amenable for efficient sampling and at the same time also maintains the desirable property of exploitation and exploration trade-off. Then, we propose a Gaussian mixture model-based random search (GMRS) algorithm. We build global convergence of both the tailored GPS algorithm and the GMRS algorithm for COvS problems. Finally, we carry out some numerical studies to illustrate the performance of the GMRS algorithm.",

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AB - This paper studies integrated random search algorithms for continuous optimization-via-simulation (COvS) problems. We first tailor the Gaussian process-based search (GPS) algorithm to handle COvS problems. We then analyze the potential sampling issue of the GPS algorithm and propose to construct a desirable Gaussian mixture model (GMM) which is amenable for efficient sampling and at the same time also maintains the desirable property of exploitation and exploration trade-off. Then, we propose a Gaussian mixture model-based random search (GMRS) algorithm. We build global convergence of both the tailored GPS algorithm and the GMRS algorithm for COvS problems. Finally, we carry out some numerical studies to illustrate the performance of the GMRS algorithm.

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Gaussian mixture model-based random search for continuous optimization via simulation

Abstract

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