Comparer des méthodes

	Optimisation par Colonies de Fourmis	Évolution Différentielle
Domaine	Optimisation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1992 (foundational thesis); 1997 (Ant Colony System formalization)	1997
Auteur d'origine	—	Rainer Storn & Kenneth Price
Type	Metaheuristic — swarm intelligence	Population-based stochastic metaheuristic
Source fondatrice	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.	Storn, R. & Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341–359.
Alias	ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system	DE algorithm, Diferansiyel Evrim (DE), DE optimization
Apparentées	5	5
Résumé	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.	Differential Evolution (DE), introduced by Rainer Storn and Kenneth Price in 1997, is a population-based stochastic optimisation algorithm designed for continuous parameter spaces. It generates candidate solutions by combining vector differences between existing population members, making it a powerful and parameter-lean alternative to Genetic Algorithms and Particle Swarm Optimisation when the search landscape is non-convex, multimodal, or poorly suited to gradient-based methods.