방법 비교

	입자 군집 최적화 (PSO)	점균 알고리즘
분야	최적화	최적화
계열	Process / pipeline	Machine learning
기원 연도	1995	2020
창시자	—	Shimin Li
유형	Population-based metaheuristic / swarm intelligence	Nature-inspired metaheuristic algorithm
원전	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.	Li, S., Chen, H., Wang, M., Heidari, A. A., & Chakraborty, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300-323.
별칭	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)	SMA
관련	6	5
요약	Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems.	The Slime Mould Algorithm (SMA) is a nature-inspired metaheuristic optimization technique introduced by Li et al. in 2020. It mimics the behavior of slime moulds, which spread and contract to find optimal food sources. SMA addresses complex optimization problems by simulating the adaptive foraging and spatial distribution patterns of these organisms.