방법 비교

	불확실성 하에서도 좋은 성능을 유지하는 해 찾기: 강건 모의 담금질	불확실성 하에서 안정적인 파레토 최적 해를 찾는 강건 다목적 최적화
분야	시뮬레이션	시뮬레이션
계열	Process / pipeline	Process / pipeline
기원 연도	1983 (SA); robust variant emerged 1990s–2000s	2006
창시자	Kirkpatrick, Gelatt & Vecchi (SA basis); robust formulation developed across the operations research community	Deb, K. & Gupta, H.
유형	Metaheuristic with robustness evaluation	Optimization framework
원전	Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.	Deb, K., & Gupta, H. (2006). Introducing robustness in multi-objective optimization. Evolutionary Computation, 14(4), 463–494.
별칭	RSA, Robust SA, Uncertainty-robust simulated annealing, Worst-case simulated annealing	RMOO, Robust MOO, Robust Pareto Optimization, Uncertainty-Robust Multi-Objective Optimization
관련	5	4
요약	Robust Simulated Annealing (RSA) adapts the classical simulated annealing metaheuristic to seek solutions that perform well not just under nominal conditions but across the full range of uncertain or adversarial parameter values. By embedding a robustness evaluation — worst-case, expected-case, or regret-based — into the SA acceptance step, RSA trades some nominal optimality for resilience, making it valuable when problem parameters are imprecisely known or subject to environmental variation.	Robust Multi-Objective Optimization (RMOO) is a framework for finding solutions that simultaneously optimize multiple conflicting objectives while remaining insensitive to perturbations in decision variables or problem parameters. Unlike classical MOO, RMOO explicitly incorporates uncertainty into the optimization loop, producing a robust Pareto front whose members perform well not only at the nominal design point but also across a neighbourhood of plausible operating conditions.