A detailed analysis of the population-based ant colony optimization algorithm for the TSP and the QAP

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.