Сравнение методов

	Bayesian NSGA-II	Многокритериальная оптимизация
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	2002–2006	1896 (concept); 1989–2002 (evolutionary algorithms era)
Автор метода	Emmerich, M. T. M. et al. (surrogate-assisted MO-EA); Deb et al. (NSGA-II base)	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Тип	Surrogate-assisted multi-objective evolutionary algorithm	Optimization framework
Основополагающий источник	Deb, K., Pratap, A., Agarwal, S., Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Другие названия	B-NSGA-II, Surrogate-Assisted NSGA-II, Gaussian Process NSGA-II, Bayesian Multi-Objective EA	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Связанные	3	3
Сводка	Bayesian NSGA-II integrates Gaussian process surrogate models (Bayesian metamodels) into the NSGA-II evolutionary loop to solve expensive multi-objective optimization problems. By replacing costly true function evaluations with fast probabilistic predictions, it discovers high-quality Pareto-front approximations with far fewer real evaluations than standard NSGA-II.	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.