Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Otimização por Enxame de Partículas (PSO)× | Algoritmo Genético× | |
|---|---|---|
| Área | Otimização | Otimização |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1995 | 1975 |
| Autor original≠ | — | John Henry Holland |
| Tipo≠ | Population-based metaheuristic / swarm intelligence | Population-based metaheuristic |
| Fonte seminal≠ | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ | Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗ |
| Outros nomes | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon |
| Relacionados≠ | 6 | 5 |
| Resumo≠ | 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. | A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail. |
| ScholarGateConjunto de dados ↗ |
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