Сравнение методов

	Стохастический NSGA-II	Робастный NSGA-II
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	2001–2002	2006
Автор метода	Deb, K. et al. (NSGA-II base); Hughes, E. J. and subsequent researchers for stochastic extensions	Kalyanmoy Deb and Himanshu Gupta
Тип	Evolutionary multi-objective optimization under uncertainty	Robust evolutionary multi-objective optimization algorithm
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., 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.
Другие названия	S-NSGA-II, NSGA-II under Uncertainty, Stochastic Multi-Objective NSGA-II, Robust NSGA-II	Robust NSGA2, NSGA-II under uncertainty, Uncertainty-aware NSGA-II, RNSGA-II
Связанные	5	5
Сводка	Stochastic NSGA-II extends the NSGA-II evolutionary algorithm to handle objective functions that are noisy, uncertain, or probabilistic. By averaging or sampling stochastic objectives across multiple evaluations, it identifies Pareto-optimal solutions that are robust to uncertainty, making it suitable for engineering design, supply chain, and policy optimization problems where real-world variability matters.	Robust NSGA-II extends the classic NSGA-II evolutionary algorithm to account for parametric uncertainty, finding Pareto-optimal trade-off solutions that remain high-performing even when input parameters deviate from their nominal values. Instead of optimizing objective values at a single point, it evaluates each candidate solution across a range or distribution of uncertainty realizations and selects for robustness alongside Pareto dominance.