Krahasoni metodat
Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.
| Optimizimi robust× | Optimizimi i bazuar në zëvendësues× | |
|---|---|---|
| Fusha | Optimizimi | Optimizimi |
| Familja | Process / pipeline | Process / pipeline |
| Viti i origjinës≠ | 1970s theoretical roots; modern tractable form from late 1990s–2004 | 1989 (computer experiments formulation) |
| Krijuesi≠ | Ben-Tal, El Ghaoui & Nemirovski (seminal book, 2009); Bertsimas & Sim (tractable polyhedral formulation, 2004) | Sacks, Welch, Mitchell & Wynn (computer experiments framework, 1989); Kriging popularised by Matheron (1963) |
| Lloji≠ | Mathematical programming framework | Metamodel-assisted black-box optimization |
| Burimi themelues≠ | Ben-Tal, A., El Ghaoui, L. & Nemirovski, A. (2009). Robust Optimization. Princeton University Press. ISBN: 9780691143682 | Forrester, A., Sobester, A., & Keane, A. (2008). Engineering Design via Surrogate Modelling: A Practical Guide. Wiley. link ↗ |
| Emërtime të tjera≠ | minimax optimization, worst-case optimization, Gürbüz Optimizasyon (Robust Optimization) | Vekil Model Tabanlı Optimizasyon (Surrogate-Based), metamodel-assisted optimization, surrogate modelling, emulator-based optimization |
| Të lidhura | 5 | 5 |
| Përmbledhja≠ | Robust optimization is a mathematical programming framework, formalised by Ben-Tal and Nemirovski in the late 1990s and made broadly tractable by Bertsimas and Sim (2004), that finds decisions guaranteed to perform acceptably under every scenario within a predefined uncertainty set — rather than assuming parameter values are known exactly. Instead of optimising for a single expected outcome, it minimises the worst-case objective across all plausible realisations of uncertain data. | Surrogate-based optimization, formalized in the computer-experiments framework of Sacks et al. (1989) and popularized for engineering by Forrester et al. (2008), replaces a prohibitively expensive simulation or physical experiment with a cheap approximate model — called a surrogate or metamodel — and then optimizes that surrogate instead. The surrogate is typically a Kriging (Gaussian Process), Radial Basis Function, or polynomial response surface fitted to a small set of carefully chosen design evaluations and periodically updated as the search progresses. |
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