Сравнение методов

	Оптимизация роя частиц для сценарного анализа политики	Генетический алгоритм для сценарного анализа политики
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1995 (PSO); applied to policy scenarios from 2000s onward	1975 (GA); 2000s (policy scenario application)
Автор метода	Kennedy, J. & Eberhart, R. (PSO); policy scenario framing from planning and operations research literature	Holland, J. H. (GA foundation); Lempert, Popper & Bankes (policy scenario search)
Тип	Metaheuristic optimization within policy scenario framework	Evolutionary metaheuristic for policy scenario exploration
Основополагающий источник	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948.	Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI. ISBN: 9780262581110
Другие названия	PS-PSO, Policy PSO, Scenario-based PSO, Policy scenario swarm optimization	PSGA, Policy-GA, Policy Optimization Genetic Algorithm, Evolutionary Policy Scenario Search
Связанные	6	4
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.	The Policy Scenario Genetic Algorithm applies evolutionary search to systematically explore large, combinatorial policy alternative spaces under multiple future scenarios. Rather than exhaustively enumerating options, it breeds successive generations of candidate policies, retaining those that perform well across scenario conditions, yielding robust, high-performing policy recommendations.