Sammenlign metoder

	Bayesiansk målprogrammering	Bayesiansk multi-objektiv optimering
Fagområde	Simulering	Simulering
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1990s	2006-2016
Ophavsperson	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
Oprindelig kilde	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.
Aliasser	BGP, Bayesian GP, Probabilistic Goal Programming, Bayesian Multi-Goal Optimization	BMOO, Bayesian MOO, Multi-objective Bayesian optimization, MOBO
Relaterede	6	3
Resumé	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.