出版社：Centro Latinoamericano de Estudios en Informática
摘要：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.
关键词：Artificial Intelligence, Ant Colony Optimization, Omicron ACO, MAX-MIN Ant
System.
