Comparer des méthodes

	Algorithme Génétique Déterministe	Optimisation par essaim particulaire déterministe
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1975–1989	1995 (PSO); deterministic formulation circa 2002
Auteur d'origine	Goldberg, D. E.; Holland, J. H.	Kennedy, J., Eberhart, R. (PSO); deterministic variant formalized in convergence analysis literature
Type	Deterministic evolutionary optimization	Swarm intelligence metaheuristic — deterministic variant
Source fondatrice	Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA. ISBN: 9780201157673	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 — International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE.
Alias	DGA, Deterministic EA, Deterministic Evolutionary Algorithm, Deterministic Selection GA	DPSO, Deterministic PSO, PSO without stochastic components, Fully Deterministic PSO
Apparentées	5	6
Résumé	A Deterministic Genetic Algorithm (DGA) applies the structural framework of evolutionary computation — population, selection, crossover, and replacement — using entirely deterministic operators and fixed decision rules instead of stochastic sampling. By eliminating randomness, the algorithm becomes fully reproducible: running it twice on the same problem yields identical solutions, making it tractable for rigorous benchmarking, reproducibility studies, and systems where stochasticity is undesirable.	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.