Сравнение методов

	Робастный имитированный отжиг	Robust Tabu Search
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1983 (SA); robust variant emerged 1990s–2000s	1989 (TS); robust variant ~2000s
Автор метода	Kirkpatrick, Gelatt & Vecchi (SA basis); robust formulation developed across the operations research community	Glover, F. (Tabu Search); robustness extensions by various authors
Тип	Metaheuristic with robustness evaluation	Metaheuristic with robustness mechanism
Основополагающий источник	Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.	Glover, F. (1989). Tabu search — Part I. ORSA Journal on Computing, 1(3), 190–206.
Другие названия	RSA, Robust SA, Uncertainty-robust simulated annealing, Worst-case simulated annealing	RTS, Robust TS, Uncertainty-aware Tabu Search, Tabu Search under Uncertainty
Связанные	5	6
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 Tabu Search (RTS) extends the classical Tabu Search metaheuristic by evaluating candidate solutions not only on their nominal objective value but also on their performance under uncertainty. Instead of seeking the best solution for a single scenario, RTS seeks solutions that perform well across a range of scenarios or realizations, trading peak optimality for reliability.