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Robusno simulirano kaljenje×Robusni genetski algoritam×
PodručjeSimulacijaSimulacija
ObiteljProcess / pipelineProcess / pipeline
Godina nastanka1983 (SA); robust variant emerged 1990s–2000s2005 (systematic survey); earlier applications from late 1990s
TvoracKirkpatrick, Gelatt & Vecchi (SA basis); robust formulation developed across the operations research communityJin, Y. and Branke, J. (systematic formalization); roots in Holland (1975)
VrstaMetaheuristic with robustness evaluationMetaheuristic evolutionary optimizer with robustness mechanism
Temeljni izvorKirkpatrick, S., Gelatt, C. D., Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680. DOI ↗Jin, Y., Branke, J. (2005). Evolutionary optimization in uncertain environments — a survey. IEEE Transactions on Evolutionary Computation, 9(3), 303–317. DOI ↗
Drugi naziviRSA, Robust SA, Uncertainty-robust simulated annealing, Worst-case simulated annealingRGA, Robust GA, Uncertainty-Aware Genetic Algorithm, Noise-Tolerant Genetic Algorithm
Srodne56
SažetakRobust 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.The Robust Genetic Algorithm (RGA) extends standard genetic algorithms to find solutions that perform well not only at the nominal design point but also when subjected to uncertainty in decision variables, parameters, or fitness evaluations. By incorporating explicit robustness measures into selection pressure, RGA balances optimality against sensitivity to perturbation, making it suitable for engineering design, scheduling, and policy optimization under real-world variability.
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ScholarGateUsporedite metode: Robust Simulated Annealing · Robust Genetic Algorithm. Preuzeto 2026-06-17 s https://scholargate.app/hr/compare