Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Robust Tabu Search× | Робастная оптимизация методами роя частиц× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1989 (TS); robust variant ~2000s | 2000s |
| Автор метода≠ | Glover, F. (Tabu Search); robustness extensions by various authors | Kennedy, J. & Eberhart, R. C. (PSO); robustness extensions by multiple authors, 2000s |
| Тип≠ | Metaheuristic with robustness mechanism | Metaheuristic — robust swarm-based optimizer |
| Основополагающий источник≠ | Glover, F. (1989). Tabu search — Part I. ORSA Journal on Computing, 1(3), 190–206. DOI ↗ | Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm Intelligence. Morgan Kaufmann Publishers. ISBN: 9781558605954 |
| Другие названия | RTS, Robust TS, Uncertainty-aware Tabu Search, Tabu Search under Uncertainty | Robust PSO, RPSO, Uncertainty-robust PSO, PSO with robustness |
| Связанные | 6 | 6 |
| Сводка≠ | 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. | 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. |
| ScholarGateНабор данных ↗ |
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