The method of multiple heterogeneous ant colonies with information exchange (MHACIE) is presented in this paper with emphasis on the speed of finding the optimal solution and the corresponding computational complexity. The proposed method which is inspired by biology and psychology has a structure composed of several ant colonies. These colonies participate in solving problems in a concurrently manner and also exchange information with each other in communicational steps. Each ant colony is considered as an intelligent agent with behavioral traits. These behavioral traits play a key role in the solving procedure, in interrelation circumstances and in installation of relations. Faster solutions have been achieved using different employments of agents in the algorithm structure. Experimental results show the superiority of Multiple heterogeneous ant colonies algorithm in comparison to the standard ant colony system (ACS) and particle swarm optimization (PSO) algorithms on different benchmarks. A dynamic, control engineering benchmark is also provided in order to gain a more complete evaluation of the proposed algorithm.