Linganisha mbinu

	Policy Scenario Particle Swarm Optimization	Uchambuzi wa Matukio ya Sera
Nyanja	Uigaji	Uigaji
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1995 (PSO); applied to policy scenarios from 2000s onward	1967–1990s
Mwanzilishi	Kennedy, J. & Eberhart, R. (PSO); policy scenario framing from planning and operations research literature	Kahn, H. & Wiener, A. J. (seminal); adapted for policy by RAND Corporation and OECD
Aina	Metaheuristic optimization within policy scenario framework	Qualitative-quantitative hybrid scenario method
Chanzo asilia	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948.	Swart, R., Raskin, P., Robinson, J. (2004). The problem of the future: sustainability science and scenario analysis. Global Environmental Change, 14(2), 137–146.
Majina mbadala	PS-PSO, Policy PSO, Scenario-based PSO, Policy scenario swarm optimization	PSA, Policy Scenarios, Policy Impact Scenario Analysis, Counterfactual Policy Analysis
Zinazohusiana	6	5
Muhtasari	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.	Policy Scenario Analysis is a structured method for evaluating how different policy interventions perform across a range of plausible future states. By pairing specific policy levers with alternative scenarios, analysts can assess robustness, trade-offs, and unintended consequences of policy choices before implementation — making it a cornerstone of evidence-based policy design in fields from climate to public health.