เปรียบเทียบวิธี

	Simulated Annealing แบบคงทน (Robust Simulated Annealing)	อัลกอริทึมพันธุกรรมที่แข็งแกร่ง
สาขาวิชา	การจำลอง	การจำลอง
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1983 (SA); robust variant emerged 1990s–2000s	2005 (systematic survey); earlier applications from late 1990s
ผู้ริเริ่ม	Kirkpatrick, Gelatt & Vecchi (SA basis); robust formulation developed across the operations research community	Jin, Y. and Branke, J. (systematic formalization); roots in Holland (1975)
ประเภท	Metaheuristic with robustness evaluation	Metaheuristic evolutionary optimizer with robustness mechanism
แหล่งต้นตำรับ	Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.	Jin, Y., Branke, J. (2005). Evolutionary optimization in uncertain environments — a survey. IEEE Transactions on Evolutionary Computation, 9(3), 303–317.
ชื่อเรียกอื่น	RSA, Robust SA, Uncertainty-robust simulated annealing, Worst-case simulated annealing	RGA, Robust GA, Uncertainty-Aware Genetic Algorithm, Noise-Tolerant Genetic Algorithm
ที่เกี่ยวข้อง	5	6
สรุป	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.	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.