Сравнение методов

	Оптимизация роя частиц для сценарного анализа политики	Оптимизация роем частиц (PSO)
Область	Имитационное моделирование	Оптимизация
Семейство	Process / pipeline	Process / pipeline
Год появления	1995 (PSO); applied to policy scenarios from 2000s onward	1995
Автор метода	Kennedy, J. & Eberhart, R. (PSO); policy scenario framing from planning and operations research literature	—
Тип	Metaheuristic optimization within policy scenario framework	Population-based metaheuristic / swarm intelligence
Основополагающий источник	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Другие названия	PS-PSO, Policy PSO, Scenario-based PSO, Policy scenario swarm optimization	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Связанные	6	6
Сводка	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.	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.