Comparer des méthodes

	Recherche Tabou Multi-objectif (RTMO)	Optimisation par Colonies de Fourmis Multi-Objectifs (MOACO)
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1997	1999
Auteur d'origine	Hansen, M. P.; building on Glover (1989) Tabu Search	Gambardella, Taillard & Agazzi; Dorigo & Stützle
Type	Metaheuristic multi-objective optimization	Population-based metaheuristic
Source fondatrice	Hansen, M. P. (1997). Tabu search for multiobjective optimization: MOTS. Presented at the 13th International Conference on Multiple Criteria Decision Making (MCDM), Cape Town, South Africa.	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.
Alias	MOTS, Multi-criteria Tabu Search, Pareto Tabu Search, TSMOO	MOACO, Multi-Objective ACO, Pareto Ant Colony Optimization, Multi-objective ACO
Apparentées	5	4
Résumé	Multi-objective Tabu Search (MOTS) is a metaheuristic algorithm that extends the classic Tabu Search framework to simultaneously optimize two or more conflicting objective functions. Instead of a single optimum, it seeks to approximate the Pareto front — the set of solutions where no objective can be improved without worsening another — making it suitable for complex combinatorial and continuous optimization problems in engineering, logistics, and operations research.	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.