Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Otimização por Enxame de Partículas para Cenários de Políticas× | Otimização por Enxame de Partículas Estocástico× | |
|---|---|---|
| Área | Simulação | Simulação |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1995 (PSO); applied to policy scenarios from 2000s onward | 1995–2002 |
| Autor original≠ | Kennedy, J. & Eberhart, R. (PSO); policy scenario framing from planning and operations research literature | Kennedy, J. and Eberhart, R. (base PSO); stochastic extensions by Clerc, Kennedy and community |
| Tipo≠ | Metaheuristic optimization within policy scenario framework | Metaheuristic optimization — stochastic swarm intelligence |
| Fonte seminal≠ | Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948. DOI ↗ | Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, Vol. 4, pp. 1942-1948. IEEE. DOI ↗ |
| Outros nomes | PS-PSO, Policy PSO, Scenario-based PSO, Policy scenario swarm optimization | Stochastic PSO, SPSO, Randomized PSO, Probabilistic PSO |
| Relacionados≠ | 6 | 4 |
| Resumo≠ | Policy Scenario Particle Swarm Optimization integrates Particle Swarm Optimization (PSO) with explicit policy scenario analysis. A swarm of candidate policy solutions is evaluated under multiple defined future scenarios, and PSO's velocity-position update rules guide the swarm toward solutions that perform well—or robustly—across all considered scenarios. It is used in energy, environmental, infrastructure, and public resource planning. | Stochastic Particle Swarm Optimization (Stochastic PSO) is a swarm-intelligence metaheuristic that extends the standard PSO framework by incorporating explicit stochastic elements — random inertia weights, probabilistic velocity resets, or noise injections — to escape local optima and maintain population diversity throughout the search. It is widely applied to continuous, mixed, and noisy optimization problems in engineering, operations research, and simulation-based design. |
| ScholarGateConjunto de dados ↗ |
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