Сравнение на методи

	Robust Tabu Search	Робастна оптимизация чрез рояк от частици
Област	Симулационно моделиране	Симулационно моделиране
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1989 (TS); robust variant ~2000s	2000s
Създател	Glover, F. (Tabu Search); robustness extensions by various authors	Kennedy, J. & Eberhart, R. C. (PSO); robustness extensions by multiple authors, 2000s
Тип	Metaheuristic with robustness mechanism	Metaheuristic — robust swarm-based optimizer
Основополагащ източник	Glover, F. (1989). Tabu search — Part I. ORSA Journal on Computing, 1(3), 190–206.	Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm Intelligence. Morgan Kaufmann Publishers. ISBN: 9781558605954
Други названия	RTS, Robust TS, Uncertainty-aware Tabu Search, Tabu Search under Uncertainty	Robust PSO, RPSO, Uncertainty-robust PSO, PSO with robustness
Свързани	6	6
Резюме	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.	Robust Particle Swarm Optimization (Robust PSO) extends the classical PSO metaheuristic to explicitly account for uncertainty in the objective function, constraints, or decision variables. Rather than optimizing a single nominal objective, each candidate solution is evaluated over a set of uncertainty scenarios, and fitness is judged by a robustness criterion such as worst-case performance or expected value, yielding solutions that remain near-optimal even when conditions deviate from nominal assumptions.