방법 비교

	베이즈 유전 알고리즘	입자 군집 최적화 (PSO)
분야	시뮬레이션	최적화
계열	Process / pipeline	Process / pipeline
기원 연도	1999	1995
창시자	Pelikan, M., Goldberg, D. E., & Cantu-Paz, E.	—
유형	Evolutionary metaheuristic with Bayesian probabilistic model	Population-based metaheuristic / swarm intelligence
원전	Pelikan, M., Goldberg, D. E., & Cantu-Paz, E. (1999). BOA: The Bayesian optimization algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999), pp. 525–532. Morgan Kaufmann.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
별칭	BGA, Bayesian-guided GA, Probabilistic GA, EDA-GA	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
관련	5	6
요약	A Bayesian Genetic Algorithm (BGA) replaces traditional crossover and mutation operators with a probabilistic Bayesian network learned from selected high-fitness individuals. At each generation the algorithm builds a graphical model of promising solution structure, then samples new offspring from that model, enabling the search to capture and exploit variable dependencies that standard GAs miss.	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.