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Evolutionary computation and swarm intelligence meta-heuristics are exceptional instances that environment has been a never-ending source of creativeness. The behavior of bees, bacteria, glow-worms, fireflies and other beings have stirred swarm intelligence scholars to create innovative optimization algorithms. This paper proposes the Meerkat Clan Algorithm (MCA) that is a novel swarm intelligence algorithm resulting from watchful observation of the Meerkat (Suricata suricatta) in the Kalahari Desert in southern Africa. This animal shows an exceptional intelligence, tactical organizational skills, and remarkable directional cleverness in its traversal of the desert when searching for food. A Meerkat Clan Algorithm (MCA) proposed to solve the optimization problems through reach the optimal solution by efficient way comparing with another swarm intelligence. Traveling Salesman Problem uses as a case study to measure the capacity of the proposed algorithm through comparing its results with another swarm intelligence. MCA shows its capacity to solve the Traveling Salesman’s Problem. Its dived the solutions group to sub-group depend of meerkat behavior that gives a good diversity to reach an optimal solution. Paralleled with the current algorithms for resolving TSP by swarm intelligence, it has been displayed that the size of the resolved problems could be enlarged by adopting the algorithm proposed here.