Recently we have setup the goal of investigating new truly distributed forms of Ant Colony Optimization. We proposed a new distributed approach for Ant Colony Optimization (ACO) algorithms called Ant Colony Optimization on a Distributed Architecture (ACODA). ACODA was designed to allow efficient implementation of ACO algorithms on state-of-the art distributed multi-agent middleware. In this paper we present experimental results that support the feasibility of ACODA by considering a distributed version of the Ant Colony System (ACS). In particular we show the effectiveness of this approach for solving Traveling Salesperson Problem by comparing experimental results of ACODA versions of distributed ACS with distributed random searches on a high-speed cluster network.