Multi-Fidelity Methods for Optimization: A Survey
<|reference_start|>Multi-Fidelity Methods for Optimization: A Survey: Real-world black-box optimization often involves time-consuming or costly experiments and simulations. Multi-fidelity optimization (MFO) stands out as a cost-effective strategy that balances high-fidelity accuracy with computational efficiency through a hierarchical fidelity approach. This survey presents a systematic exploration of MFO, underpinned by a novel text mining framework based on a pre-trained language model. We delve deep into the foundational principles and methodologies of MFO, focusing on three core components -- multi-fidelity surrogate models, fidelity management strategies, and optimization techniques. Additionally, this survey highlights the diverse applications of MFO across several key domains, including machine learning, engineering design optimization, and scientific discovery, showcasing the adaptability and effectiveness of MFO in tackling complex computational challenges. Furthermore, we also envision several emerging challenges and prospects in the MFO landscape, spanning scalability, the composition of lower fidelities, and the integration of human-in-the-loop approaches at the algorithmic level. We also address critical issues related to benchmarking and the advancement of open science within the MFO community. Overall, this survey aims to catalyze further research and foster collaborations in MFO, setting the stage for future innovations and breakthroughs in the field.<|reference_end|>
@article{li2024multi-fidelity,
    title={Multi-Fidelity Methods for Optimization: A Survey},
    author={Ke Li and Fan Li},
    journal={arXiv preprint arXiv:2402.09638},
    year={2024},
    number={COLALab Report #2024005},
    archivePrefix={arXiv},
    eprint={2402.09638},
    primaryClass={cs.LG cs.NE}
}