Gaussian Process-Based Random Search for Continuous Optimization via Simulation
Xiuxian Wang, L. Jeff Hong, Zhibin Jiang, Haihui Shen
Abstract
A gaussian process-based random search framework for continuous optimization via simulation Stochastic 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.