Comparer des méthodes

	Optimisation par Colonies de Fourmis Multi-Objectifs (MOACO)	Recuit Simulé Multi-Objectif (MOSA)
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1999	1992–1998
Auteur d'origine	Gambardella, Taillard & Agazzi; Dorigo & Stützle	Serafini, P.; Czyzak, P. and Jaszkiewicz, A.
Type	Population-based metaheuristic	Metaheuristic / Pareto-based optimizer
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.	Czyzak, P., Jaszkiewicz, A. (1998). Pareto simulated annealing — a metaheuristic technique for multiple-objective combinatorial optimization. Journal of Multi-Criteria Decision Analysis, 7(1), 34–47.
Alias	MOACO, Multi-Objective ACO, Pareto Ant Colony Optimization, Multi-objective ACO	MOSA, Multi-Criteria Simulated Annealing, Pareto Simulated Annealing, PSA
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 Simulated Annealing (MOSA) extends the classical simulated annealing metaheuristic to problems with two or more conflicting objective functions. Instead of converging to a single optimum, MOSA explores the solution space stochastically and maintains an archive of non-dominated (Pareto-optimal) solutions, offering decision-makers a diverse trade-off front rather than one prescribed answer.