Comparar métodos

	Anneamento Simulado Robusto	Otimização Robusta por Enxame de Partículas
Área	Simulação	Simulação
Família	Process / pipeline	Process / pipeline
Ano de origem	1983 (SA); robust variant emerged 1990s–2000s	2000s
Autor original	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
Tipo	Metaheuristic with robustness evaluation	Metaheuristic — robust swarm-based optimizer
Fonte seminal	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
Outros nomes	RSA, Robust SA, Uncertainty-robust simulated annealing, Worst-case simulated annealing	Robust PSO, RPSO, Uncertainty-robust PSO, PSO with robustness
Relacionados	5	6
Resumo	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.