方法对比
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| 贝叶斯多目标优化× | 随机多目标优化× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2006-2016 | 1990s–2000s |
| 提出者≠ | Emmerich, M.; Svenson, J.; and related Gaussian process optimization community | Various (Fonseca, Fleming, Deb, Zitzler, and others) |
| 类型≠ | Surrogate-model-assisted multi-objective optimizer | Stochastic metaheuristic optimization |
| 开创性文献≠ | Svenson, J., Santner, T. (2016). Multiobjective optimization of expensive-to-evaluate deterministic computer simulator models. Computational Statistics & Data Analysis, 94, 250-264. DOI ↗ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| 别名 | BMOO, Bayesian MOO, Multi-objective Bayesian optimization, MOBO | SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization |
| 相关≠ | 3 | 5 |
| 摘要≠ | 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. |
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