Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Kupungua kwa Nguvu kwa Kuthibitishwa× | Algorithmu ya Kijeni ya Imara× | |
|---|---|---|
| Nyanja | Uigaji | Uigaji |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1983 (SA); robust variant emerged 1990s–2000s | 2005 (systematic survey); earlier applications from late 1990s |
| Mwanzilishi≠ | Kirkpatrick, Gelatt & Vecchi (SA basis); robust formulation developed across the operations research community | Jin, Y. and Branke, J. (systematic formalization); roots in Holland (1975) |
| Aina≠ | Metaheuristic with robustness evaluation | Metaheuristic evolutionary optimizer with robustness mechanism |
| Chanzo asilia≠ | Kirkpatrick, 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 ↗ |
| Majina mbadala | RSA, Robust SA, Uncertainty-robust simulated annealing, Worst-case simulated annealing | RGA, Robust GA, Uncertainty-Aware Genetic Algorithm, Noise-Tolerant Genetic Algorithm |
| Zinazohusiana≠ | 5 | 6 |
| Muhtasari≠ | 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. |
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