Multi-Fidelity Stochastic Trust Region Method with Adaptive Sampling

TL;DR

This paper introduces ASTRO-MFDF, an adaptive multi-fidelity trust-region method that intelligently combines high- and low-fidelity simulations to optimize complex systems efficiently while mitigating bias from low-fidelity models.

Contribution

The paper presents a novel adaptive sampling trust-region algorithm that selectively uses low-fidelity models based on correlation, improving efficiency and accuracy in multi-fidelity simulation optimization.

Findings

ASTRO-MFDF reduces computational costs compared to traditional methods.

The method effectively mitigates bias from low-fidelity models.

Numerical experiments demonstrate improved optimization performance.

Abstract

Simulation optimization is often hindered by the high cost of running simulations. Multi-fidelity methods offer a promising solution by incorporating cheaper, lower-fidelity simulations to reduce computational time. However, the bias in low-fidelity models can mislead the search, potentially steering solutions away from the high-fidelity optimum. To overcome this, we propose ASTRO-MFDF, an adaptive sampling trust-region method for multi-fidelity simulation optimization. ASTRO-MFDF features two key strategies: (i) it adaptively determines the sample size and selects appropriate sampling strategies to reduce computational cost; and (ii) it selectively uses low-fidelity information only when a high correlation with the high-fidelity is anticipated, reducing the risk of bias. We validate the performance and computational efficiency of ASTRO-MFDF through numerical experiments using the SimOpt library.