방법 비교

	정책 시나리오 입자 군집 최적화	정책 시나리오 유전 알고리즘
분야	시뮬레이션	시뮬레이션
계열	Process / pipeline	Process / pipeline
기원 연도	1995 (PSO); applied to policy scenarios from 2000s onward	1975 (GA); 2000s (policy scenario application)
창시자	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)
유형	Metaheuristic optimization within policy scenario framework	Evolutionary metaheuristic for policy scenario exploration
원전	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
별칭	PS-PSO, Policy PSO, Scenario-based PSO, Policy scenario swarm optimization	PSGA, Policy-GA, Policy Optimization Genetic Algorithm, Evolutionary Policy Scenario Search
관련	6	4
요약	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.