So sánh phương pháp

	Tối ưu hóa đa mục tiêu Bayes	Tối ưu hóa Bayes
Lĩnh vực	Mô phỏng	Tối ưu hóa
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2006-2016	1975 (foundational); 2012 (ML standard)
Người khởi xướng	Emmerich, M.; Svenson, J.; and related Gaussian process optimization community	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)
Loại	Surrogate-model-assisted multi-objective optimizer	Sequential model-based black-box optimization
Công trình gốc	Svenson, J., Santner, T. (2016). Multiobjective optimization of expensive-to-evaluate deterministic computer simulator models. Computational Statistics & Data Analysis, 94, 250-264.	Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25.
Tên gọi khác	BMOO, Bayesian MOO, Multi-objective Bayesian optimization, MOBO	Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO
Liên quan	3	2
Tóm tắt	Bayesian Multi-Objective Optimization (BMOO/MOBO) uses Gaussian process surrogate models to approximate multiple expensive objective functions and guides the search toward the Pareto frontier with minimal real evaluations. By quantifying prediction uncertainty at each candidate point, it balances exploration of unknown regions against exploitation of promising solutions, making it especially powerful when each function evaluation is computationally or experimentally costly.	Bayesian Optimization is a sequential, model-based strategy for finding the optimum of expensive black-box functions with as few evaluations as possible. Rooted in the work of Mockus (1975) and brought to mainstream machine-learning practice by Snoek, Larochelle, and Adams (2012), it fits a probabilistic surrogate model — typically a Gaussian Process — to past observations and uses an acquisition function to decide where to probe next, balancing exploration of unknown regions with exploitation of promising ones.