Methoden vergleichen

	Stochastic NSGA-II	Stochastische Multi-Objektiv-Optimierung
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2001–2002	1990s–2000s
Urheber	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
Wegweisende Quelle	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. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Aliasnamen	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
Verwandt	5	5
Zusammenfassung	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.