Strategies for Parallel Ant Colony Optimization on Graphics Processing Units

Ant colony algorithms are known to have a significant ability of finding high-quality solutions in a reasonable time [2]. However, the computational time of these methods is seriously compromised when the current instance of the problem has a high dimension and/or is hard to solve. In this line, a significant amount of research has been done in order to reduce computation time and improve the solution quality of ACO algorithms by using parallel computing. Due to the independence of the artificial ants, which are guided by an indirect communication via their environment (pheromone trail and heuristic information), ACO algorithms are naturally suitable for parallel implementation.