Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Algoritmo Genético Estocástico× | Otimização por Enxame de Partículas (PSO)× | |
|---|---|---|
| Área≠ | Simulação | Otimização |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1975 | 1995 |
| Autor original≠ | Holland, J. H. | — |
| Tipo≠ | Stochastic evolutionary metaheuristic | Population-based metaheuristic / swarm intelligence |
| Fonte seminal≠ | Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. ISBN: 978-0262581110 | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ |
| Outros nomes≠ | SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) |
| Relacionados≠ | 5 | 6 |
| Resumo≠ | 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. | 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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