Salīdzināt metodes

	Politikas scenāriju daļiņu baru optimizācija	Stohastiskā daļiņu baru optimizācija
Nozare	Simulācija	Simulācija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1995 (PSO); applied to policy scenarios from 2000s onward	1995–2002
Autors	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
Tips	Metaheuristic optimization within policy scenario framework	Metaheuristic optimization — stochastic swarm intelligence
Pirmavots	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. Proceedings of ICNN'95 - International Conference on Neural Networks, Vol. 4, pp. 1942-1948. IEEE.
Citi nosaukumi	PS-PSO, Policy PSO, Scenario-based PSO, Policy scenario swarm optimization	Stochastic PSO, SPSO, Randomized PSO, Probabilistic PSO
Saistītās	6	4
Kopsavilkums	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.