The clustering problem is among the most important optimization problems. Given that it is an NP-hard problem, it can be efficiently solved using meta-heuristic algorithms such as the gravitation search algorithm (GSA). GSA is a new swarm-based algorithm particularly suitable for solving NP-hard combinatorial optimization problems. This paper solves the clustering problem with a newly proposed memetic GSA (MGSA) algorithm. MGSA is coupled with the pattern reduction operator and the multi-start operator. The proposed MGSA algorithm was verified on six UCI benchmarks and images segmentation. Based on a performance comparison amongst MGSA, the original GSA, and two state-of-the-art meta-heuristic algorithms (Firefly algorithm and the Artificial bee colony algorithm), we observe that the proposed algorithm can significantly reduce computation time without compromising much on the quality of the solution.