@Incollection{dorigo03a,
  author =	 {Marco Dorigo and Thomas St{\"u}tzle},
  title =	 {The Ant Colony Optimization Metaheuristic:
                  Algorithms, Applications and Advances},
  booktitle =	 {Handbook of Metaheuristics},
  pages =	 {251--285},
  year =	 2003,
  editor =	 {F. Glover and G. Kochenberger},
  volume =	 57,
  crossref = 	 {glover03a},
  series =	 {International Series in Operations Research \&
                  Management Science},
  publisher =	 {Kluwer Academic Publishers, Norwell, {MA}},
  abstract =	 {Ant Colony Optimization (ACO) [31, 32] is a recently
                  proposed metaheuristic approach for solving hard
                  combinatorial optimization problems. The inspiring
                  source of ACO is the pheromone trail laying and
                  following behavior of real ants which use pheromones
                  as a communication medium. In analogy to the
                  biological example, ACO is based on the indirect
                  communication of a colony of simple agents, called
                  (artificial) ants, mediated by (artificial)
                  pheromone trails. The pheromone trails in ACO serve
                  as a distributed, numerical information which the
                  ants use to probabilistically construct solutions to
                  the problem being solved and which the ants adapt
                  during the algorithm’s execution to reflect their
                  search experience.},
  url = 	 {http://jmvidal.cse.sc.edu/library/dorigo03a.pdf},
  cluster = 	 {1385744196167022699}
}