Methoden vergleichen

	Deterministic Particle Swarm Optimization	Stochastische Partikelschwarmoptimierung
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1995 (PSO); deterministic formulation circa 2002	1995–2002
Urheber	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
Typ	Swarm intelligence metaheuristic — deterministic variant	Metaheuristic optimization — stochastic swarm intelligence
Wegweisende Quelle	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.
Aliasnamen	DPSO, Deterministic PSO, PSO without stochastic components, Fully Deterministic PSO	Stochastic PSO, SPSO, Randomized PSO, Probabilistic PSO
Verwandt	6	4
Zusammenfassung	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.