A Consistency-Centric Approach to Set-Based Optimization with Multiple Models of Unranked Fidelity

TL;DR

This paper introduces S-BOMM, a set-based optimization method that leverages model consistency across multiple fidelity models without assuming a single most accurate model, demonstrated to be effective empirically.

Contribution

It proposes a novel set-based optimization approach that does not rely on a presumed high-fidelity model, using consistency among models to identify solutions.

Findings

Probabilistic bounds on correctness of the method.

Empirical validation on test problems shows effectiveness.

Method offers a practical alternative in multi-model optimization scenarios.

Abstract

In complex real-world settings, optimization is challenged by the presence of diverse models of differing fidelity. In many optimization problems, a single model is treated as the most accurate representation of the underlying system, while other models are evaluated primarily by their agreement with this presumed most accurate model. Yet in real-world applications, model accuracy is rarely known a priori and assuming a single most accurate model can be misleading. This paper addresses this gap by proposing a flexible set-based optimization methodology called Set-Based Optimization with Multiple Models (S-BOMM) that works with multiple models without the assumption of a most accurate high-fidelity model. Unlike traditional optimization approaches that focus on finding an optimal solution according to the high-fidelity model, our methodology utilizes consistency between models to identify good solutions across multiple models. A probabilistic analysis of the consistency method is provided that bounds the likelihood of the methodology producing correct or incorrect results. Empirical results demonstrate the effectiveness of S-BOMM on test problems. By focusing on the consistency across models rather than relying on a single best solution, this set-based approach offers a practical alternative to optimization problems where multiple models must be considered without assuming a single most accurate high-fidelity model.