Omicron ACO. A New Ant Colony Optimization Algorithm

Authors

  • Benjamın Baran Universidad Nacional de Asuncion Centro Nacional de Computacion
  • Osvaldo Gomez Universidad Nacional de Asuncion Centro Nacional de Computacion

DOI:

https://doi.org/10.19153/cleiej.8.1.5

Keywords:

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

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.

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Published

2018-07-28

Issue

Vol. 8 No. 1 (2005): Special Issue of Best Papers presented at CLEI2004, Arequipa, Peru

Section

Articles

License

CLEIej is supported by its home institution, CLEI, and by the contribution of the Latin American and international researchers community, and it does not apply any author charges whatsoever for submitting and publishing. Since its creation in 1998, all contents are made publicly accesibly. The current license being applied is a (CC)-BY license (effective October 2015; between 2011 and 2015 a (CC)-BY-NC license was used).