Comparar métodos

	Optimización Bayesiana Multi-Objetivo	Optimización Estocástica Multiobjetivo
Campo	Simulación	Simulación
Familia	Process / pipeline	Process / pipeline
Año de origen	2006-2016	1990s–2000s
Autor original	Emmerich, M.; Svenson, J.; and related Gaussian process optimization community	Various (Fonseca, Fleming, Deb, Zitzler, and others)
Tipo	Surrogate-model-assisted multi-objective optimizer	Stochastic metaheuristic optimization
Fuente seminal	Svenson, J., Santner, T. (2016). Multiobjective optimization of expensive-to-evaluate deterministic computer simulator models. Computational Statistics & Data Analysis, 94, 250-264.	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Alias	BMOO, Bayesian MOO, Multi-objective Bayesian optimization, MOBO	SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization
Relacionados	3	5
Resumen	Bayesian Multi-Objective Optimization (BMOO/MOBO) uses Gaussian process surrogate models to approximate multiple expensive objective functions and guides the search toward the Pareto frontier with minimal real evaluations. By quantifying prediction uncertainty at each candidate point, it balances exploration of unknown regions against exploitation of promising solutions, making it especially powerful when each function evaluation is computationally or experimentally costly.	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.