Сравнение методов

	Детерминированное многокритериальное оптимизация	Стохастическая многокритериальная оптимизация
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1951–1999	1990s–2000s
Автор метода	Kuhn, H. W., Tucker, A. W. (Pareto optimality formalized); Miettinen, K. (systematic deterministic framework)	Various (Fonseca, Fleming, Deb, Zitzler, and others)
Тип	Optimization framework — deterministic Pareto and scalarization methods	Stochastic metaheuristic optimization
Основополагающий источник	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 978-0-471-87339-6	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Другие названия	Deterministic MOO, Classical Multi-Objective Optimization, Non-Stochastic MOO, Deterministic Pareto Optimization	SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization
Связанные	3	5
Сводка	Deterministic Multi-Objective Optimization (Deterministic MOO) is a family of classical optimization approaches that simultaneously minimize or maximize multiple conflicting objective functions over a deterministic feasible set. It produces a Pareto front — the set of non-dominated solutions — from which a decision-maker selects the preferred trade-off. Unlike stochastic variants, all objective evaluations and constraints are fixed and noise-free.	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.