Сравнение методов

	Байесовская многокритериальная оптимизация	Стохастическая многокритериальная оптимизация
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	2006-2016	1990s–2000s
Автор метода	Emmerich, M.; Svenson, J.; and related Gaussian process optimization community	Various (Fonseca, Fleming, Deb, Zitzler, and others)
Тип	Surrogate-model-assisted multi-objective optimizer	Stochastic metaheuristic optimization
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	SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization
Связанные	3	5
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.	Stochastic Multi-Objective Optimization (SMOO) is a class of methods that simultaneously optimizes two or more conflicting objectives when parameters, costs, or constraints are uncertain or random. Rather than a single optimal solution, it produces a Pareto front of non-dominated solutions, each representing a different balance among objectives under the modeled uncertainty.