Representative Calibration Using Black-Box Optimization and Clustering
Representative Calibration Using Black-Box Optimization and Clustering
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DOI：
10.1109/wsc60868.2023.10408638
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发表时间：
2023-12
期刊：
影响因子：
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通讯作者：
Serin Lee;Pariyakorn Maneekul;Z. Zabinsky
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文献类型：
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作者：
Serin Lee;Pariyakorn Maneekul;Z. Zabinsky
校准是模型有效性的关键步骤，但它的代表性往往被忽视。本文提出了一种两阶段的方法来校准模型，通过识别多个不同的参数集，同时保持计算效率的目标数据。第一阶段采用黑盒优化算法生成近似最优的参数集，第二阶段聚类生成的参数集。五个黑盒优化算法，即拉丁超立方体采样（LHS），顺序模型为基础的算法配置（SMAC），Optuna，模拟退火（SA），遗传算法（GA），进行了测试和比较，使用疾病意见房室模型预测健康结果。结果表明，LHS和Optuna允许更多的探索，并在未来可能的健康结果中捕获更多的变化。SMAC、SA和GA更善于找到最佳参数集，但它们的采样方法产生的模型结果差异较小。这种两阶段的方法可以减少计算时间，同时产生鲁棒性和代表性的校准。
Calibration is a crucial step for model validity, yet its representation is often disregarded. This paper proposes a two-stage approach to calibrate a model that represents target data by identifying multiple diverse parameter sets while remaining computationally efficient. The first stage employs a black-box optimization algorithm to generate near-optimal parameter sets, the second stage clusters the generated parameter sets. Five black-box optimization algorithms, namely, Latin Hypercube Sampling (LHS), Sequential Model-based Algorithm Configuration (SMAC), Optuna, Simulated Annealing (SA), and Genetic Algorithm (GA), are tested and compared using a disease-opinion compartmental model with predicted health outcomes. Results show that LHS and Optuna allow more exploration and capture more variety in possible future health outcomes. SMAC, SA, and GA, are better at finding the best parameter set but their sampling approach generates less diverse model outcomes. This two-stage approach can reduce computation time while producing robust and representative calibration.