Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Стохастичний пошук з табу× | Оптимізація роєм частинок (PSO)× | |
|---|---|---|
| Галузь≠ | Імітаційне моделювання | Оптимізація |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1990s | 1995 |
| Автор методу≠ | Glover, F. (base TS); stochastic extensions by various authors (1990s–2000s) | — |
| Тип≠ | Stochastic metaheuristic optimizer | Population-based metaheuristic / swarm intelligence |
| Основоположне джерело≠ | Glover, F. (1990). Tabu search: A tutorial. Interfaces, 20(4), 74-94. DOI ↗ | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ |
| Інші назви≠ | STS, Randomized Tabu Search, Probabilistic Tabu Search, Noisy Tabu Search | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) |
| Пов'язані≠ | 5 | 6 |
| Підсумок≠ | 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. | Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems. |
| ScholarGateНабір даних ↗ |
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