قارن الطرق

	الخوارزمية الجينية العشوائية	التحسين العشوائي لـ سرب الجسيمات
المجال	المحاكاة	المحاكاة
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	1975	1995–2002
صاحب الطريقة	Holland, J. H.	Kennedy, J. and Eberhart, R. (base PSO); stochastic extensions by Clerc, Kennedy and community
النوع	Stochastic evolutionary metaheuristic	Metaheuristic optimization — stochastic swarm intelligence
المصدر التأسيسي	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. Proceedings of ICNN'95 - International Conference on Neural Networks, Vol. 4, pp. 1942-1948. IEEE.
الأسماء البديلة	SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm	Stochastic PSO, SPSO, Randomized PSO, Probabilistic PSO
ذات صلة	5	4
الملخص	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.	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.