Comparer des méthodes

	Recherche Tabou	Optimisation par Colonies de Fourmis
Domaine	Optimisation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1989	1992 (foundational thesis); 1997 (Ant Colony System formalization)
Auteur d'origine	Fred Glover	—
Type	Local-search metaheuristic	Metaheuristic — swarm intelligence
Source fondatrice	Glover, F. (1989). Tabu Search — Part I. ORSA Journal on Computing, 1(3), 190–206.	Dorigo, M. & Gambardella, L.M. (1997). Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation, 1(1), 53-66.
Alias	Tabu Araması (Tabu Search), TS, tabu metaheuristic	ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system
Apparentées	4	5
Résumé	Tabu Search is a local-search metaheuristic introduced by Fred Glover in 1989 that uses a tabu list — a short-term memory of recently visited solutions — to prevent cycling and escape local optima. By explicitly forbidding moves that reverse recent decisions, the algorithm explores the search space more broadly and, through long-term memory structures such as aspiration criteria, aims to approach the global optimum even in large, complex combinatorial problems.	Ant Colony Optimization (ACO) is a metaheuristic algorithm introduced by Marco Dorigo and colleagues in the early 1990s that solves combinatorial optimisation problems by simulating the collective foraging behaviour of ants. Real ants lay pheromone trails on paths and preferentially follow stronger trails; ACO turns this positive-feedback mechanism into a search procedure that finds high-quality solutions to graph-structured problems such as the Travelling Salesman Problem, vehicle routing, and scheduling.