方法对比
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| 鲁棒禁忌搜索× | 稳健遗传算法× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1989 (TS); robust variant ~2000s | 2005 (systematic survey); earlier applications from late 1990s |
| 提出者≠ | Glover, F. (Tabu Search); robustness extensions by various authors | Jin, Y. and Branke, J. (systematic formalization); roots in Holland (1975) |
| 类型≠ | Metaheuristic with robustness mechanism | Metaheuristic evolutionary optimizer with robustness mechanism |
| 开创性文献≠ | Glover, F. (1989). Tabu search — Part I. ORSA Journal on Computing, 1(3), 190–206. DOI ↗ | Jin, Y., Branke, J. (2005). Evolutionary optimization in uncertain environments — a survey. IEEE Transactions on Evolutionary Computation, 9(3), 303–317. DOI ↗ |
| 别名 | RTS, Robust TS, Uncertainty-aware Tabu Search, Tabu Search under Uncertainty | RGA, Robust GA, Uncertainty-Aware Genetic Algorithm, Noise-Tolerant Genetic Algorithm |
| 相关 | 6 | 6 |
| 摘要≠ | Robust Tabu Search (RTS) extends the classical Tabu Search metaheuristic by evaluating candidate solutions not only on their nominal objective value but also on their performance under uncertainty. Instead of seeking the best solution for a single scenario, RTS seeks solutions that perform well across a range of scenarios or realizations, trading peak optimality for reliability. | 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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