Monte Carlo Integration with adaptive variance selection for improved stochastic Efficient Global Optimization

TL;DR

This paper introduces an adaptive variance selection method for Monte Carlo integration within a stochastic optimization framework, improving efficiency and robustness in high-dimensional stochastic problems.

Contribution

It proposes an adaptive scheme for selecting target variance in Monte Carlo integration, enhancing stochastic global optimization performance over fixed variance approaches.

Findings

Outperforms constant variance methods in benchmark tests

Enables application to high-dimensional stochastic problems

Reduces computational cost through adaptive variance control

Abstract

In this paper, the minimization of computational cost on evaluating multi-dimensional integrals is explored. More specifically, a method based on an adaptive scheme for error variance selection in Monte Carlo integration (MCI) is presented. It uses a stochastic Efficient Global Optimization (sEGO) framework to guide the optimization search. The MCI is employed to approximate the integrals, because it provides the variance of the error in the integration. In the proposed approach, the variance of the integration error is included into a Stochastic Kriging framework by setting a target variance in the MCI. We show that the variance of the error of the MCI may be controlled by the designer and that its value strongly influences the computational cost and the exploration ability of the optimization process. Hence, we propose an adaptive scheme for automatic selection of the target variance during the sEGO search. The robustness and efficiency of the proposed adaptive approach were evaluated on global optimization stochastic benchmark functions as well as on a tuned mass damper design problem. The results showed that the proposed adaptive approach consistently outperformed the constant approach and a multi-start optimization method. Moreover, the use of MCI enabled the method application in problems with high number of stochastic dimensions. On the other hand, the main limitation of the method is inherited from sEGO coupled with the Kriging metamodel: the efficiency of the approach is reduced when the number of design variables increases.