Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Annealing Simulado× | Otimização por Enxame de Partículas (PSO)× | |
|---|---|---|
| Área | Otimização | Otimização |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1983 | 1995 |
| Autor original | — | — |
| Tipo≠ | Probabilistic metaheuristic / local search | Population-based metaheuristic / swarm intelligence |
| Fonte seminal≠ | Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. DOI ↗ | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ |
| Outros nomes | Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) |
| Relacionados≠ | 5 | 6 |
| Resumo≠ | 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. | 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. |
| ScholarGateConjunto de dados ↗ |
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