A review on the ant colony optimization metaheuristic: basis, models and new trends.

Cordón, Oscar, Herrera, Francisco, and Stützle, Thomas. "A review on the ant colony optimization metaheuristic: basis, models and new trends.." Mathware and Soft Computing 9.2-3 (2002): 141-175. <http://eudml.org/doc/39241>.

@article{Cordón2002,
abstract = {Ant Colony Optimization (ACO) is a recent metaheuristic method that is inspired by the behavior of real ant colonies. In this paper, we review the underlying ideas of this approach that lead from the biological inspiration to the ACO metaheuristic, which gives a set of rules of how to apply ACO algorithms to challenging combinatorial problems. We present some of the algorithms that were developed under this framework, give an overview of current applications, and analyze the relationship between ACO and some of the best known metaheuristics. In addition, we describe recent theoretical developments in the field and we conclude by showing several new trends and new research directions in this field.},
author = {Cordón, Oscar, Herrera, Francisco, Stützle, Thomas},
journal = {Mathware and Soft Computing},
keywords = {Optimización global; Algoritmo de búsqueda; Problemas combinatorios; Heurística},
language = {eng},
number = {2-3},
pages = {141-175},
title = {A review on the ant colony optimization metaheuristic: basis, models and new trends.},
url = {http://eudml.org/doc/39241},
volume = {9},
year = {2002},
}

TY - JOUR
AU - Cordón, Oscar
AU - Herrera, Francisco
AU - Stützle, Thomas
TI - A review on the ant colony optimization metaheuristic: basis, models and new trends.
JO - Mathware and Soft Computing
PY - 2002
VL - 9
IS - 2-3
SP - 141
EP - 175
AB - Ant Colony Optimization (ACO) is a recent metaheuristic method that is inspired by the behavior of real ant colonies. In this paper, we review the underlying ideas of this approach that lead from the biological inspiration to the ACO metaheuristic, which gives a set of rules of how to apply ACO algorithms to challenging combinatorial problems. We present some of the algorithms that were developed under this framework, give an overview of current applications, and analyze the relationship between ACO and some of the best known metaheuristics. In addition, we describe recent theoretical developments in the field and we conclude by showing several new trends and new research directions in this field.
LA - eng
KW - Optimización global; Algoritmo de búsqueda; Problemas combinatorios; Heurística
UR - http://eudml.org/doc/39241
ER -