Zhou, Enlu and Bhatnagar, Shalabh (2018) Gradient-Based Adaptive Stochastic Search for Simulation Optimization Over Continuous Space. In: INFORMS JOURNAL ON COMPUTING, 30 (1). pp. 154-167.
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We extend the idea of model-based algorithms for deterministic optimization to simulation optimization over continuous space. Model-based algorithms iteratively generate a population of candidate solutions from a sampling distribution and use the performance of the candidate solutions to update the sampling distribution. By viewing the original simulation optimization problem as another optimization problem over the parameter space of the sampling distribution, we propose to use a direct gradient search on the parameter space to update the sampling distribution. To improve the computational efficiency, we further develop a two-timescale updating scheme that updates the parameter on a slow timescale and estimates the quantities involved in the parameter updating on the fast timescale. We analyze the convergence properties of our algorithms through techniques from stochastic approximation, and demonstrate the good empirical performance by comparing with two state-of-the-art model-based simulation optimization methods.
| Item Type: | Journal Article |
|---|---|
| Publication: | INFORMS JOURNAL ON COMPUTING |
| Publisher: | INFORMS, 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA |
| Additional Information: | Copy right for the article belong to INFORMS, 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA |
| Department/Centre: | Division of Electrical Sciences > Computer Science & Automation |
| Date Deposited: | 02 Mar 2018 14:50 |
| Last Modified: | 02 Mar 2018 14:50 |
| URI: | http://eprints.iisc.ac.in/id/eprint/59066 |
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