Сравнение методов

	Байесовская многокритериальная оптимизация	Многокритериальная оптимизация
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	2006-2016	1896 (concept); 1989–2002 (evolutionary algorithms era)
Автор метода	Emmerich, M.; Svenson, J.; and related Gaussian process optimization community	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Тип	Surrogate-model-assisted multi-objective optimizer	Optimization framework
Основополагающий источник	Svenson, J., Santner, T. (2016). Multiobjective optimization of expensive-to-evaluate deterministic computer simulator models. Computational Statistics & Data Analysis, 94, 250-264.	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Другие названия	BMOO, Bayesian MOO, Multi-objective Bayesian optimization, MOBO	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Связанные	3	3
Сводка	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.	Multi-Objective Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis.