Compară metode

	Optimizare Robustă prin Roi de Particule	Annealing Simulat Robust
Domeniu	Simulare	Simulare
Familie	Process / pipeline	Process / pipeline
Anul apariției	2000s	1983 (SA); robust variant emerged 1990s–2000s
Autorul original	Kennedy, J. & Eberhart, R. C. (PSO); robustness extensions by multiple authors, 2000s	Kirkpatrick, Gelatt & Vecchi (SA basis); robust formulation developed across the operations research community
Tip	Metaheuristic — robust swarm-based optimizer	Metaheuristic with robustness evaluation
Sursa seminală	Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm Intelligence. Morgan Kaufmann Publishers. ISBN: 9781558605954	Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.
Denumiri alternative	Robust PSO, RPSO, Uncertainty-robust PSO, PSO with robustness	RSA, Robust SA, Uncertainty-robust simulated annealing, Worst-case simulated annealing
Înrudite	6	5
Rezumat	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.	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.