Title :
Ant colony optimization: a new meta-heuristic
Author :
Dorigo, Marco ; Di Caro, Gianni
Author_Institution :
IRIDIA, Univ. Libre de Bruxelles, Belgium
Abstract :
Recently, a number of algorithms inspired by the foraging behavior of ant colonies have been applied to the solution of difficult discrete optimization problems. We put these algorithms in a common framework by defining the Ant Colony Optimization (ACO) meta-heuristic. A couple of paradigmatic examples of applications of these novel meta-heuristic are given, as well as a brief overview of existing applications
Keywords :
artificial life; evolutionary computation; heuristic programming; set theory; ACO; ant colony optimization; discrete optimization problems; foraging behavior; meta-heuristic; novel meta-heuristic; paradigmatic examples; Ant colony optimization; Circuits; Cities and towns; Cost function; Routing; Time measurement; Traveling salesman problems; Vehicles;
Conference_Titel :
Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5536-9
DOI :
10.1109/CEC.1999.782657
