手法を比較

	確率的遺伝的アルゴリズム	Particle Swarm Optimization (PSO)
分野	シミュレーション	最適化
系統	Process / pipeline	Process / pipeline
提唱年	1975	1995
提唱者	Holland, J. H.	—
種類	Stochastic evolutionary metaheuristic	Population-based metaheuristic / swarm intelligence
原典	Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. ISBN: 978-0262581110	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
別名	SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
関連	5	6
概要	The Stochastic Genetic Algorithm (SGA) is a population-based metaheuristic that mimics biological evolution — selection, crossover, and mutation — to search for near-optimal solutions in complex, nonlinear, or combinatorial spaces. Its randomized operators make it robust to local optima and broadly applicable across engineering, scheduling, machine learning, and operations research.	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.