Comparer des méthodes

	Optimisation par Colonies de Fourmis Multi-Objectifs (MOACO)	Optimisation par essaims particulaires multi-objectif (MOPSO)
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1999	2004
Auteur d'origine	Gambardella, Taillard & Agazzi; Dorigo & Stützle	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.
Type	Population-based metaheuristic	Population-based swarm metaheuristic
Source fondatrice	Gambardella, L. M., Taillard, E., & Agazzi, G. (1999). MACS-VRPTW: A multiple ant colony system for vehicle routing problems with time windows. In D. Corne, M. Dorigo, & F. Glover (Eds.), New Ideas in Optimization (pp. 63–76). McGraw-Hill.	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.
Alias	MOACO, Multi-Objective ACO, Pareto Ant Colony Optimization, Multi-objective ACO	MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO
Apparentées	4	5
Résumé	Multi-Objective Ant Colony Optimization (MOACO) is a swarm-intelligence metaheuristic that extends the classic Ant Colony Optimization framework to simultaneously optimize two or more conflicting objectives. Artificial ants construct candidate solutions guided by pheromone trails and heuristic information, progressively building an archive of Pareto-optimal solutions rather than converging to a single best answer.	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.