手法を比較

	ロバスト最適化	代理モデルベース最適化
分野	最適化	最適化
系統	Process / pipeline	Process / pipeline
提唱年	1970s theoretical roots; modern tractable form from late 1990s–2004	1989 (computer experiments formulation)
提唱者	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)
種類	Mathematical programming framework	Metamodel-assisted black-box optimization
原典	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.
別名	minimax optimization, worst-case optimization, Gürbüz Optimizasyon (Robust Optimization)	Vekil Model Tabanlı Optimizasyon (Surrogate-Based), metamodel-assisted optimization, surrogate modelling, emulator-based optimization
関連	5	5
概要	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.