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 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.