The graph-partitioning problem arises as a fundamental problem in many important scientific and engineering applications. A variety of optimization methods are used for solving this problem and among them the metaheuristics outstand for its efficiency and robustness. Here we address the performance of the Distributed Multilevel Ant-Colony Algorithm (DMACA), a metaheuristic approach for solving the multi-way graph partitioning problem, which is based on the ant-colony optimization paradigm and is integrated with a multilevel procedure. The basic idea of the DMACA consists of parallel, independent runs enhanced with cooperation in the form of a solution exchange among the concurrent searches. The objective of the DMACA is to reduce the overall computation time, while preserving the quality of the solutions obtained by the sequential version. The experimental evaluation on a 2-way and 4-way partitioning with 1% and 5% imbalance confirms that with respect to the sequential version, the DMACA obtains statistically, equally good solutions at a 99% confidence level within a reduced overall computation time.