Comparer des méthodes

	Optimisation multi-objectif	Optimisation par essaim particulaire (PSO)
Domaine	Simulation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1896 (concept); 1989–2002 (evolutionary algorithms era)	1995
Auteur d'origine	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.	—
Type	Optimization framework	Population-based metaheuristic / swarm intelligence
Source fondatrice	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Alias	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Apparentées	3	6
Résumé	Multi-Objective Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis.	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.