Population-Based Ant Colony Optimization for Sequential Ordering Problem

The population-based ant colony optimization (PACO) algorithm uses a pheromone memory model based on a population of solutions stored in a solution archive. Pheromone updates in the PACO are performed only when a solution enters or leaves the archive. Absence of the local pheromone update rule makes the pheromone memory less flexible compared to other ACO algorithms but saves computational time. In this work, we present a novel application of the PACO for solving the sequential ordering problem (SOP). In particular, we investigate how different values of the PACO parameters affect its performance and identify some problems regarding the diversity of solutions stored in the solution archive. A comparison with the state-of-the-art algorithm for the SOP shows that the PACO can be a very competitive tool.