par Dorigo, Marco 
;Birattari, Mauro ;Stützle, Thomas
Référence IEEE computational intelligence magazine, 1, 4, page (28-39)
Publication Publié, 2006
;Birattari, Mauro ;Stützle, Thomas Référence IEEE computational intelligence magazine, 1, 4, page (28-39)
Publication Publié, 2006
Article révisé par les pairs
| Résumé : | The introduction of ant colony optimization (ACO) and to survey its most notable applications are discussed. Ant colony optimization takes inspiration from the forging behavior of some ant species. These ants deposit Pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony. The model proposed by Deneubourg and co-workers for explaining the foraging behavior of ants is the main source of inspiration for the development of ant colony optimization. In ACO a number of artificial ants build solutions to an optimization problem and exchange information on their quality through a communication scheme that is reminiscent of the one adopted by real ants. ACO algorithms is introduced and all ACO algorithms share the same idea and the ACO is formalized into a meta-heuristics for combinatorial problems. It is foreseeable that future research on ACO will focus more strongly on rich optimization problems that include stochasticity. |
