Сравнение методов

	Оптимизация роя частиц для сценарного анализа политики	Многокритериальная оптимизация роем частиц (MOPSO)
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1995 (PSO); applied to policy scenarios from 2000s onward	2004
Автор метода	Kennedy, J. & Eberhart, R. (PSO); policy scenario framing from planning and operations research literature	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.
Тип	Metaheuristic optimization within policy scenario framework	Population-based swarm metaheuristic
Основополагающий источник	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.
Другие названия	PS-PSO, Policy PSO, Scenario-based PSO, Policy scenario swarm optimization	MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO
Связанные	6	5
Сводка	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.