Volume 8 : Number 1 : Paper 5
August 2005 Special Issue of Best Papers presented at CLEI2004, Arequipa, Peru
Title:
Omicron ACO. A New Ant Colony Optimization Algorithm

Authors and Affiliations:
Osvaldo Gomez,
Benjamin Baran, National Computing Center, National University of Asuncion, Paraguay

Abstract:
Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies that has been successful in the resolution of hard combinatorial optimization problems like the
Traveling Salesman Problem (TSP). This paper proposes the Omicron ACO (OA), a novel population-based ACO alternative originally designed as an analytical tool. To experimentally prove OA advantages, this work compares the behavior between the OA and the MMAS as a function of time in two well-known TSP problems. A simple study of the behavior of OA as a function of its parameters shows its robustness.

Keywords: Artificial Intelligence, Ant Colony Optimization, Omicron ACO, MAX-MIN Ant System.


Received June, 23, 2005, Revised August, 19, 2005 , Editor: Mauricio Solar
Full paper, 8 pages [ PDF, 396 Kb ]