Сравнение на методи

	Сценарна оптимизация на политики чрез рояк частици	Оптимизация чрез рояк от частици (PSO)
Област	Симулационно моделиране	Оптимизация
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1995 (PSO); applied to policy scenarios from 2000s onward	1995
Създател	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
Основополагащ източник	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.
Други названия	PS-PSO, Policy PSO, Scenario-based PSO, Policy scenario swarm optimization	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Свързани	6	6
Резюме	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.