Сравнение методов

	Робастная оптимизация методами роя частиц	Стохастическая оптимизация роем частиц
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	2000s	1995–2002
Автор метода	Kennedy, J. & Eberhart, R. C. (PSO); robustness extensions by multiple authors, 2000s	Kennedy, J. and Eberhart, R. (base PSO); stochastic extensions by Clerc, Kennedy and community
Тип	Metaheuristic — robust swarm-based optimizer	Metaheuristic optimization — stochastic swarm intelligence
Основополагающий источник	Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm Intelligence. Morgan Kaufmann Publishers. ISBN: 9781558605954	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, Vol. 4, pp. 1942-1948. IEEE.
Другие названия	Robust PSO, RPSO, Uncertainty-robust PSO, PSO with robustness	Stochastic PSO, SPSO, Randomized PSO, Probabilistic PSO
Связанные	6	4
Сводка	Robust Particle Swarm Optimization (Robust PSO) extends the classical PSO metaheuristic to explicitly account for uncertainty in the objective function, constraints, or decision variables. Rather than optimizing a single nominal objective, each candidate solution is evaluated over a set of uncertainty scenarios, and fitness is judged by a robustness criterion such as worst-case performance or expected value, yielding solutions that remain near-optimal even when conditions deviate from nominal assumptions.	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.