Comparer des méthodes

	Programmation par objectifs bayésienne	Optimisation bayésienne multi-objectifs
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s	2006-2016
Auteur d'origine	Rios Insua, D. and colleagues	Emmerich, M.; Svenson, J.; and related Gaussian process optimization community
Type	Multi-objective optimization under uncertainty	Surrogate-model-assisted multi-objective optimizer
Source fondatrice	Rios Insua, D. (1990). Sensitivity Analysis in Multi-objective Decision Making. Springer-Verlag, Berlin. ISBN: 9783540528814	Svenson, J., Santner, T. (2016). Multiobjective optimization of expensive-to-evaluate deterministic computer simulator models. Computational Statistics & Data Analysis, 94, 250-264.
Alias	BGP, Bayesian GP, Probabilistic Goal Programming, Bayesian Multi-Goal Optimization	BMOO, Bayesian MOO, Multi-objective Bayesian optimization, MOBO
Apparentées	6	3
Résumé	Bayesian Goal Programming (BGP) integrates Bayesian statistical inference with classic goal programming to handle uncertainty in targets and parameters. Instead of treating goal thresholds as fixed constants, BGP encodes them as probability distributions, updates beliefs using observed data, and then solves the resulting probabilistic optimization problem to find solutions that satisfy multiple aspirational goals under uncertainty.	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.