Comparer des méthodes

	Optimisation par essaims particulaires multi-objectif (MOPSO)	Optimisation par essaim particulaire (PSO)
Domaine	Simulation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2004	1995
Auteur d'origine	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.	—
Type	Population-based swarm metaheuristic	Population-based metaheuristic / swarm intelligence
Source fondatrice	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256–279.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Alias	MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Apparentées	5	6
Résumé	Multi-Objective Particle Swarm Optimization (MOPSO) is a swarm-intelligence metaheuristic that extends the original Particle Swarm Optimization (PSO) to handle multiple conflicting objective functions simultaneously. It maintains an external Pareto archive and uses dominance-based selection to guide a population of candidate solutions toward the true Pareto front without requiring a priori preference information.	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.