Сравнение на методи

	Robust Simulated Annealing	Робастна оптимизация чрез рояк от частици
Област	Симулационно моделиране	Симулационно моделиране
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1983 (SA); robust variant emerged 1990s–2000s	2000s
Създател	Kirkpatrick, Gelatt & Vecchi (SA basis); robust formulation developed across the operations research community	Kennedy, J. & Eberhart, R. C. (PSO); robustness extensions by multiple authors, 2000s
Тип	Metaheuristic with robustness evaluation	Metaheuristic — robust swarm-based optimizer
Основополагащ източник	Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.	Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm Intelligence. Morgan Kaufmann Publishers. ISBN: 9781558605954
Други названия	RSA, Robust SA, Uncertainty-robust simulated annealing, Worst-case simulated annealing	Robust PSO, RPSO, Uncertainty-robust PSO, PSO with robustness
Свързани	5	6
Резюме	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 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.