Сравнение методов

	Детерминизированная оптимизация роем частиц	Стохастическая оптимизация роем частиц
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1995 (PSO); deterministic formulation circa 2002	1995–2002
Автор метода	Kennedy, J., Eberhart, R. (PSO); deterministic variant formalized in convergence analysis literature	Kennedy, J. and Eberhart, R. (base PSO); stochastic extensions by Clerc, Kennedy and community
Тип	Swarm intelligence metaheuristic — deterministic variant	Metaheuristic optimization — stochastic swarm intelligence
Основополагающий источник	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. Proceedings of ICNN'95 - International Conference on Neural Networks, Vol. 4, pp. 1942-1948. IEEE.
Другие названия	DPSO, Deterministic PSO, PSO without stochastic components, Fully Deterministic PSO	Stochastic PSO, SPSO, Randomized PSO, Probabilistic PSO
Связанные	6	4
Сводка	Deterministic Particle Swarm Optimization (DPSO) removes the stochastic random coefficients from classical PSO, replacing them with fixed cognitive and social acceleration parameters. Particles move through the search space following fully predictable trajectories, enabling reproducible convergence analysis and guaranteed termination behavior in continuous and combinatorial optimization problems.	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.