Сравнение методов

	Робастный имитированный отжиг	Робастная многокритериальная оптимизация
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1983 (SA); robust variant emerged 1990s–2000s	2006
Автор метода	Kirkpatrick, Gelatt & Vecchi (SA basis); robust formulation developed across the operations research community	Deb, K. & Gupta, H.
Тип	Metaheuristic with robustness evaluation	Optimization framework
Основополагающий источник	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.
Другие названия	RSA, Robust SA, Uncertainty-robust simulated annealing, Worst-case simulated annealing	RMOO, Robust MOO, Robust Pareto Optimization, Uncertainty-Robust Multi-Objective Optimization
Связанные	5	4
Сводка	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.