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EnglishThe 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.
| Titolo: | Analysis of the population-based ant colony optimization algorithm for the TSP and the QAP |
| Autore/i: | Sabrina Oliveira; Mohamed Saifullah Hussin; Andrea Roli; Marco Dorigo; Thomas Stuetzle |
| Autore/i Unibo: | |
| Anno: | 2017 |
| Titolo del libro: | IEEE Congress on Evolutionary Computation (CEC) 2017 |
| Pagina iniziale: | 1734 |
| Pagina finale: | 1741 |
| 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. |
| Data stato definitivo: | 30-nov-2017 |
| Appare nelle tipologie: | 4.01 Contributo in Atti di convegno |
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http://hdl.handle.net/11585/612413
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