Gaussian Process-Based Random Search for Continuous Optimization via Simulation

  • Wang X
  • Hong L
  • Jiang Z
  • et al.
N/ACitations
Citations of this article
9Readers
Mendeley users who have this article in their library.
Get full text

Abstract

A gaussian process-based random search framework for continuous optimization via simulationStochastic optimization via simulation (OvS) is widely used for optimizing the performances of complex systems with continuous decision variables. Because of the existence of simulation noise and infinite feasible solutions, it is challenging to design an efficient mechanism to do the searching and estimation simultaneously to find the optimal solutions. In “Gaussian process-based random search for continuous optimization via simulation,” Wang et al. propose a Gaussian process-based random search (GPRS) framework for the design of single-observation and adaptive continuous OvS algorithms. This framework builds a Gaussian process surrogate model to estimate the objective function value of every solution based on a single observation of each sampled solution in each iteration and allow for a wide range of sampling distributions. They prove the global convergence and analyze the rate of convergence for algorithms under the GPRS framework. They also give a specific example of GPRS algorithms and validate its theoretical properties and practical efficiency using numerical experiments.Random search is an important category of algorithms to solve continuous optimization via simulation problems. To design an efficient random search algorithm, the handling of the triple “E” (i.e., exploration, exploitation and estimation) is critical. The first two E’s refer to the design of sampling distribution to balance explorative and exploitative searches, whereas the third E refers to the estimation of objective function values based on noisy simulation observations. In this paper, we propose a class of Gaussian process-based random search (GPRS) algorithms, which provide a new framework to handle the triple “E.” In each iteration, algorithms under the framework build a Gaussian process surrogate model to estimate the objective function based on single observation of each sampled solution and randomly sample solutions from a lower-bounded sampling distribution. Under the assumption of heteroscedastic and known simulation noise, we prove the global convergence of GPRS algorithms. Moreover, for Gaussian processes having continuously differentiable sample paths, we show that the rate of convergence of GPRS algorithms can be no slower than [Formula: see text]. Then, we introduce a specific GPRS algorithm to show how to design an integrated GPRS algorithm with adaptive sampling distributions and how to implement the algorithm efficiently. Numerical experiments show that the algorithm has good performances, even for problems where the variances of simulation noises are unknown.Funding: This work was supported by the National Natural Science Foundation of China [Grants 72031007, 72091211, 71931007].Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2021.0303 .

Cite

CITATION STYLE

APA

Wang, X., Hong, L. J., Jiang, Z., & Shen, H. (2023). Gaussian Process-Based Random Search for Continuous Optimization via Simulation. Operations Research. https://doi.org/10.1287/opre.2021.0303

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free