Comparer des méthodes

	NSGA-III	Optimisation par essaim particulaire (PSO)
Domaine	Recherche opérationnelle	Optimisation
Famille	Machine learning	Process / pipeline
Année d'origine	2014	1995
Auteur d'origine	Kalyanmoy Deb and Himanshu Jain	—
Type	algorithm	Population-based metaheuristic / swarm intelligence
Source fondatrice	Deb, K., & Jain, H. (2014). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577-601.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Alias	NSGA-III algorithm, NSGA-III evolutionary, many-objective optimization	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Apparentées	2	6
Résumé	NSGA-III (Non-dominated Sorting Genetic Algorithm III), developed by Kalyanmoy Deb and Himanshu Jain in 2014, is a state-of-the-art evolutionary algorithm for many-objective optimization problems. It extends the popular NSGA-II algorithm with reference-point-based selection, enabling effective handling of problems with three or more conflicting objectives.	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.