השוואת שיטות

	אלגוריתם גנטי סטוכסטי	אופטימיזציית נחיל חלקיקים (PSO)
תחום	סימולציה	אופטימיזציה
משפחה	Process / pipeline	Process / pipeline
שנת המקור	1975	1995
הוגה השיטה	Holland, J. H.	—
סוג	Stochastic evolutionary metaheuristic	Population-based metaheuristic / swarm intelligence
מקור מכונן	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.
כינויים	SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
קשורות	5	6
תקציר	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.