方法对比
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| 贝叶斯目标规划× | 贝叶斯多目标优化× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1990s | 2006-2016 |
| 提出者≠ | Rios Insua, D. and colleagues | Emmerich, M.; Svenson, J.; and related Gaussian process optimization community |
| 类型≠ | Multi-objective optimization under uncertainty | Surrogate-model-assisted multi-objective optimizer |
| 开创性文献≠ | 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. DOI ↗ |
| 别名 | BGP, Bayesian GP, Probabilistic Goal Programming, Bayesian Multi-Goal Optimization | BMOO, Bayesian MOO, Multi-objective Bayesian optimization, MOBO |
| 相关≠ | 6 | 3 |
| 摘要≠ | 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. |
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