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) uses a very different pheromone update when compared to other ACO algorithms. In this work, we study P-ACO's behavior for solving the traveling salesman problem (TSP) and the quadratic assignment problem (QAP). 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 whose parameters and behavior depend strongly on the problem tackled and on whether a local search is used.