Comparer des méthodes

	Optimisation par essaim particulaire pour les scénarios de politique	Optimisation par essaims particulaires multi-objectif (MOPSO)
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1995 (PSO); applied to policy scenarios from 2000s onward	2004
Auteur d'origine	Kennedy, J. & Eberhart, R. (PSO); policy scenario framing from planning and operations research literature	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.
Type	Metaheuristic optimization within policy scenario framework	Population-based swarm metaheuristic
Source fondatrice	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948.	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256–279.
Alias	PS-PSO, Policy PSO, Scenario-based PSO, Policy scenario swarm optimization	MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO
Apparentées	6	5
Résumé	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.	Multi-Objective Particle Swarm Optimization (MOPSO) is a swarm-intelligence metaheuristic that extends the original Particle Swarm Optimization (PSO) to handle multiple conflicting objective functions simultaneously. It maintains an external Pareto archive and uses dominance-based selection to guide a population of candidate solutions toward the true Pareto front without requiring a priori preference information.