Comparar métodos

	Optimización Estocástica por Enjambre de Partículas	Optimización por Enjambre de Partículas (PSO)
Campo	Simulación	Optimización
Familia	Process / pipeline	Process / pipeline
Año de origen	1995–2002	1995
Autor original	Kennedy, J. and Eberhart, R. (base PSO); stochastic extensions by Clerc, Kennedy and community	—
Tipo	Metaheuristic optimization — stochastic swarm intelligence	Population-based metaheuristic / swarm intelligence
Fuente seminal	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, Vol. 4, pp. 1942-1948. IEEE.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Alias	Stochastic PSO, SPSO, Randomized PSO, Probabilistic PSO	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Relacionados	4	6
Resumen	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.	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.