The population-based ant colony optimization algorithm (P-ACO) differs from other ACO algorithms through its implementation of the pheromone update management. P-ACO keeps track of a population of solutions, which serves as an archive of solutions generated by the ants’ colony. Pheromone updates in P-ACO are only done based on solutions that enter or leave the solution archive. The population-based scheme reduces considerably the computation time needed for the pheromone update when compared to ACO algorithms such as Ant System. In this work, we study P-ACO’s behavior for solving the traveling salesman problem and the quadratic assignment problem. In particular, we investigate the impact of a local search on P-ACO parameters and performance. The results clearly show that P-ACO is a very competitive tool in which its parameters and behavior depend strongly on the problem tackled and whether or not a local search is used.
A detailed analysis of the population-based ant colony optimization algorithm for the TSP and the QAP / S.M. Oliveira; M.S. Hussin; T. Stuetzle; A. Roli; M. Dorigo. - STAMPA. - (2011), pp. 13-14. (Intervento presentato al convegno GECCO 2011 tenutosi a Dublin, Ireland nel July 12-16, 2011) [10.1145/2001858.2001866].
A detailed analysis of the population-based ant colony optimization algorithm for the TSP and the QAP
ROLI, ANDREA;
2011
Abstract
The population-based ant colony optimization algorithm (P-ACO) differs from other ACO algorithms through its implementation of the pheromone update management. P-ACO keeps track of a population of solutions, which serves as an archive of solutions generated by the ants’ colony. Pheromone updates in P-ACO are only done based on solutions that enter or leave the solution archive. The population-based scheme reduces considerably the computation time needed for the pheromone update when compared to ACO algorithms such as Ant System. In this work, we study P-ACO’s behavior for solving the traveling salesman problem and the quadratic assignment problem. In particular, we investigate the impact of a local search on P-ACO parameters and performance. The results clearly show that P-ACO is a very competitive tool in which its parameters and behavior depend strongly on the problem tackled and whether or not a local search is used.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
