Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Stochastic NSGA-II× | Stochastická multikriteriální optimalizace× | |
|---|---|---|
| Obor | Simulace | Simulace |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | 2001–2002 | 1990s–2000s |
| Tvůrce≠ | Deb, K. et al. (NSGA-II base); Hughes, E. J. and subsequent researchers for stochastic extensions | Various (Fonseca, Fleming, Deb, Zitzler, and others) |
| Typ≠ | Evolutionary multi-objective optimization under uncertainty | Stochastic metaheuristic optimization |
| Původní zdroj≠ | 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. DOI ↗ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| Další názvy | S-NSGA-II, NSGA-II under Uncertainty, Stochastic Multi-Objective NSGA-II, Robust NSGA-II | SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | 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. | 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. |
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