Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Стохастическая многокритериальная оптимизация× | Стохастическое динамическое программирование× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1990s–2000s | 1957 |
| Автор метода≠ | Various (Fonseca, Fleming, Deb, Zitzler, and others) | Bellman, R.; formalized for stochastic settings by Puterman, M. L. |
| Тип≠ | Stochastic metaheuristic optimization | Sequential optimization under uncertainty |
| Основополагающий источник≠ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 |
| Другие названия | SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization | SDP, Markov Decision Process, MDP, Stochastic DP |
| Связанные≠ | 5 | 6 |
| Сводка≠ | 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. | Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving recursive value equations over states and time periods. |
| ScholarGateНабор данных ↗ |
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