Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Căutare Tabu Stocastică× | Algoritm Genetic Stocastic× | |
|---|---|---|
| Domeniu | Simulare | Simulare |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1990s | 1975 |
| Autorul original≠ | Glover, F. (base TS); stochastic extensions by various authors (1990s–2000s) | Holland, J. H. |
| Tip≠ | Stochastic metaheuristic optimizer | Stochastic evolutionary metaheuristic |
| Sursa seminală≠ | Glover, F. (1990). Tabu search: A tutorial. Interfaces, 20(4), 74-94. DOI ↗ | Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. ISBN: 978-0262581110 |
| Denumiri alternative | STS, Randomized Tabu Search, Probabilistic Tabu Search, Noisy Tabu Search | SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm |
| Înrudite | 5 | 5 |
| Rezumat≠ | Stochastic Tabu Search (STS) is an extension of classical Tabu Search that introduces randomness into the neighborhood exploration and move-selection phases. By combining tabu memory — which forbids recently visited solutions — with probabilistic acceptance or random candidate sampling, STS escapes local optima more effectively and explores rugged solution landscapes that deterministic TS may fail to traverse. | The Stochastic Genetic Algorithm (SGA) is a population-based metaheuristic that mimics biological evolution — selection, crossover, and mutation — to search for near-optimal solutions in complex, nonlinear, or combinatorial spaces. Its randomized operators make it robust to local optima and broadly applicable across engineering, scheduling, machine learning, and operations research. |
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