Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Оптимизация роем частиц (PSO)× | Имитация отжига× | |
|---|---|---|
| Область | Оптимизация | Оптимизация |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1995 | 1983 |
| Автор метода | — | — |
| Тип≠ | Population-based metaheuristic / swarm intelligence | Probabilistic metaheuristic / local search |
| Основополагающий источник≠ | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ | Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. DOI ↗ |
| Другие названия | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) | Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search |
| Связанные≠ | 6 | 5 |
| Сводка≠ | 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. | Simulated annealing is a probabilistic local-search metaheuristic introduced by Kirkpatrick, Gelatt, and Vecchi in 1983. It models the physical annealing process in metallurgy — where a material is heated and then slowly cooled to reach a low-energy crystalline state — and uses this analogy to escape local optima in combinatorial and continuous optimization problems. |
| ScholarGateНабор данных ↗ |
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