Comparer des méthodes

	NSGA-II	Optimisation par essaim particulaire (PSO)
Domaine	Optimisation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2002	1995
Auteur d'origine	—	—
Type	Evolutionary multi-objective optimisation algorithm	Population-based metaheuristic / swarm intelligence
Source fondatrice	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. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Alias	NSGA2, Non-dominated Sorting GA II, NSGA-II — Çok Amaçlı Evrimsel Optimizasyon	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Apparentées	4	6
Résumé	NSGA-II (Non-dominated Sorting Genetic Algorithm II) is the standard reference algorithm for multi-objective evolutionary optimisation, introduced by Deb, Pratap, Agarwal and Meyarivan in 2002. Rather than collapsing multiple conflicting objectives into a single score, it evolves a population of candidate solutions across generations and returns a set of Pareto-optimal trade-off solutions — the Pareto front — using fast non-dominated sorting and a crowding distance metric to preserve diversity.	Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems.