方法对比

	政策情景粒子群优化	随机粒子群优化
领域	仿真	仿真
方法族	Process / pipeline	Process / pipeline
起源年份	1995 (PSO); applied to policy scenarios from 2000s onward	1995–2002
提出者	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
类型	Metaheuristic optimization within policy scenario framework	Metaheuristic optimization — stochastic swarm intelligence
开创性文献	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.
别名	PS-PSO, Policy PSO, Scenario-based PSO, Policy scenario swarm optimization	Stochastic PSO, SPSO, Randomized PSO, Probabilistic PSO
相关	6	4
摘要	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.