Comparer des méthodes

	Optimisation par essaim d'abeilles artificielles (ABC)	Optimisation par Colonies de Fourmis
Domaine	Optimisation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2007	1992 (foundational thesis); 1997 (Ant Colony System formalization)
Auteur d'origine	Dervis Karaboga & Bahriye Basturk	—
Type	Swarm Intelligence Metaheuristic	Metaheuristic — swarm intelligence
Source fondatrice	Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.	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	ABC Algorithm, Bee Colony Optimization, Swarm-Based Bee Search, Yapay Arı Kolonisi	ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system
Apparentées	3	5
Résumé	Artificial Bee Colony (ABC) is a population-based swarm intelligence metaheuristic introduced by Karaboga and Basturk in 2007. It models the cooperative foraging behavior of a honey bee colony to search for optimal solutions in continuous numerical optimization problems. The algorithm divides candidate solutions among three bee types — employed, onlooker, and scout — and iteratively refines them through local search and probabilistic selection, making it well-suited for researchers and engineers tackling complex, multimodal optimization landscapes.	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.