방법 비교

	확률적 입자 군집 최적화	다목적 입자 군집 최적화 (MOPSO)
분야	시뮬레이션	시뮬레이션
계열	Process / pipeline	Process / pipeline
기원 연도	1995–2002	2004
창시자	Kennedy, J. and Eberhart, R. (base PSO); stochastic extensions by Clerc, Kennedy and community	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.
유형	Metaheuristic optimization — stochastic swarm intelligence	Population-based swarm metaheuristic
원전	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, Vol. 4, pp. 1942-1948. IEEE.	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.
별칭	Stochastic PSO, SPSO, Randomized PSO, Probabilistic PSO	MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO
관련	4	5
요약	Stochastic Particle Swarm Optimization (Stochastic PSO) is a swarm-intelligence metaheuristic that extends the standard PSO framework by incorporating explicit stochastic elements — random inertia weights, probabilistic velocity resets, or noise injections — to escape local optima and maintain population diversity throughout the search. It is widely applied to continuous, mixed, and noisy optimization problems in engineering, operations research, and simulation-based design.	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.