Parallelization strategies for the ant system

The Ant System is a new meta-heuristic method particularly appropriate to solve hard combinatorial optimization problems. It is a population-based, nature-inspired approach exploiting positive feedback as well as local information and has been applied successfully to a variety of combinatorial optimization problem classes. The Ant System consists of a set of cooperating agents (artificial ants) and a set of rules that determine the generation, update and usage of local and global information in order to find good solutions. As the structure of the Ant System highly suggests a parallel implementation of the algorithm, in this paper two parallelization strategies for an Ant System implementation are developed and evaluated: the synchronous parallel algorithm and the partially asynchronous parallel algorithm. Using the Traveling Salesman Problem a discrete event simulation is performed, and both strategies are evaluated on the criteria "speedup", "efficiency" and "efficacy". Finally further improvements for an advanced parallel implementation are discussed.