Analytical benchmark problems and methodological framework for the assessment and comparison of multifidelity optimization methods
L. Mainini, A. Serani, H. Pehlivan, F. Di Fiore, M. P. Rumpfkeil, E. Minisci, D. Quagliarella, S. Yildiz 等 15 位作者
This paper introduces a comprehensive benchmarking framework that includes: (i) a suite of analytical benchmark optimization problems designed to stress-test and validate multifidelity methods; (ii) a set of assessment metrics for quantifying and comparing performance over measurable objectives; and (iii) the classification, evaluation, and comparison of several families of multifidelity optimization methods and frameworks using the proposed benchmarks to identify their respective strengths and weaknesses in real-world scenarios. The proposed benchmark problems are analytically defined functions carefully selected to capture mathematical challenges commonly encountered in real-world applications, including high dimensionality, multimodality, discontinuities, and noise. Their closed-form nature ensures computational efficiency, high reproducibility, and a clear separation of algorithmic behavior from numerical artifacts. The accompanying performance metrics support the systematic evaluation of multifidelity methods, measuring both optimization effectiveness and global approximation accuracy. By providing a rigorous, reproducible, and accessible benchmarking framework, this work aims to enable the broader community to understand, compare, and advance multifidelity optimization methods for complex problems in science and engineering.
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