The population-based ant colony optimization algorithm (P-ACO) differs from other ACO algorithms because of its implementation of the pheromone update. 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 classical ACO algorithms such as Ant System. In this work, we study the behavior of P-ACO when solving the traveling salesman and the quadratic assignment problem. In particular, we investigate the impact of a local search on P-ACO parameters and performance. The results show that P-ACO reaches competitive performance but that the parameter settings and algorithm behavior are strongly problem-dependent.
Analysis of the population-based ant colony optimization algorithm for the TSP and the QAP / Sabrina, Oliveira; Mohamed Saifullah Hussin, ; Roli, Andrea; Marco, Dorigo; Thomas, Stuetzle. - STAMPA. - (2017), pp. 1734-1741. (Intervento presentato al convegno IEEE Congress on Evolutionary Computation tenutosi a Donostia - San Sebastián (Spagna) nel 5 June 2017 - 8 June 2017).
Analysis of the population-based ant colony optimization algorithm for the TSP and the QAP
Andrea Roli;
2017
Abstract
The population-based ant colony optimization algorithm (P-ACO) differs from other ACO algorithms because of its implementation of the pheromone update. 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 classical ACO algorithms such as Ant System. In this work, we study the behavior of P-ACO when solving the traveling salesman and the quadratic assignment problem. In particular, we investigate the impact of a local search on P-ACO parameters and performance. The results show that P-ACO reaches competitive performance but that the parameter settings and algorithm behavior are strongly problem-dependent.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.