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Оптимизация роя частиц для сценарного анализа политики×Стохастическая оптимизация роем частиц×
ОбластьИмитационное моделированиеИмитационное моделирование
СемействоProcess / pipelineProcess / pipeline
Год появления1995 (PSO); applied to policy scenarios from 2000s onward1995–2002
Автор методаKennedy, J. & Eberhart, R. (PSO); policy scenario framing from planning and operations research literatureKennedy, J. and Eberhart, R. (base PSO); stochastic extensions by Clerc, Kennedy and community
ТипMetaheuristic optimization within policy scenario frameworkMetaheuristic optimization — stochastic swarm intelligence
Основополагающий источникKennedy, 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 ↗
Другие названияPS-PSO, Policy PSO, Scenario-based PSO, Policy scenario swarm optimizationStochastic PSO, SPSO, Randomized PSO, Probabilistic PSO
Связанные64
СводкаPolicy 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.
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  2. 2 Источники
  3. PUBLISHED
  1. v1
  2. 2 Источники
  3. PUBLISHED

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ScholarGateСравнение методов: Policy Scenario Particle Swarm Optimization · Stochastic Particle Swarm Optimization. Получено 2026-06-19 из https://scholargate.app/ru/compare