Salīdzināt metodes

	Stochastic NSGA-II	Stohastiskā daļiņu baru optimizācija
Nozare	Simulācija	Simulācija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2001–2002	1995–2002
Autors	Deb, K. et al. (NSGA-II base); Hughes, E. J. and subsequent researchers for stochastic extensions	Kennedy, J. and Eberhart, R. (base PSO); stochastic extensions by Clerc, Kennedy and community
Tips	Evolutionary multi-objective optimization under uncertainty	Metaheuristic optimization — stochastic swarm intelligence
Pirmavots	Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, Vol. 4, pp. 1942-1948. IEEE.
Citi nosaukumi	S-NSGA-II, NSGA-II under Uncertainty, Stochastic Multi-Objective NSGA-II, Robust NSGA-II	Stochastic PSO, SPSO, Randomized PSO, Probabilistic PSO
Saistītās	5	4
Kopsavilkums	Stochastic NSGA-II extends the NSGA-II evolutionary algorithm to handle objective functions that are noisy, uncertain, or probabilistic. By averaging or sampling stochastic objectives across multiple evaluations, it identifies Pareto-optimal solutions that are robust to uncertainty, making it suitable for engineering design, supply chain, and policy optimization problems where real-world variability matters.	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.