Сравнение методов

	Детерминированное многокритериальное оптимизация	Многокритериальная оптимизация
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1951–1999	1896 (concept); 1989–2002 (evolutionary algorithms era)
Автор метода	Kuhn, H. W., Tucker, A. W. (Pareto optimality formalized); Miettinen, K. (systematic deterministic framework)	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Тип	Optimization framework — deterministic Pareto and scalarization methods	Optimization framework
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	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Связанные	3	3
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.	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.