title: Ant colony optimization and stochastic gradient descent.
abstract: In this article, we study the relationship between the two techniques known as ant colony optimization (ACO) and stochastic gradient descent. More precisely, we show that some empirical ACO algorithms approximate stochastic gradient descent in the space of pheromones, and we propose an implementation of stochastic gradient descent that belongs to the family of ACO algorithms. We then use this insight to explore the mutual contributions of the two techniques.
authors.affiliation: IRIDIA, Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, CP 194/6, B-1050 Brussels, Belgium. nmeulau@iridia.ulb.ac.be
authors.forename: Nicolas
authors.initials: N
authors.lastname: Meuleau
authors.forename: Marco
authors.initials: M
authors.lastname: Dorigo
mesh_terms: D000465:Algorithms; D000818:Animals; D001000:Ants / Q000502:physiology; D001185:Artificial Intelligence; D008962:Models, Theoretical; D012919:Social Behavior; D013269:Stochastic Processes