Comparer des méthodes

	Optimisation stochastique multi-objectifs	Simulation de Monte-Carlo
Domaine	Simulation	Prise de décision
Famille	Process / pipeline	MCDM
Année d'origine	1990s–2000s	1949
Auteur d'origine	Various (Fonseca, Fleming, Deb, Zitzler, and others)	Metropolis, N., Ulam, S.
Type	Stochastic metaheuristic optimization	Robustness wrapper — Monte Carlo uncertainty propagation
Source fondatrice	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Alias	SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization	—
Apparentées	5	0
Résumé	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.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.