Interacted Multiple Ant Colonies Optimization Framework: an Experimental Study of the Evaluation and the Exploration Techniques to Control the Search Stagnation

Search stagnation is a serious problem that all Ant Colony Optimization (ACO) algorithms suffer from regardless of their application domain. The framework of Interacted Multiple Ant Colonies Optimization (IMACO) is a recent proposition. It divides the ants’ population into several colonies and employs certain techniques to organize the work of these colonies. This paper proposes new effective evaluation and exploration techniques for IMACO and experimentally tests the stagnation behavior of IMACO. The performance of IMACO was demonstrated by comparing it with the best performing ant algorithms like Ant Colony System (ACS) and Max-Min Ant System (MMAS). The Computational results show the superiority of IMACO. The results comparison shows that IMACO with the proposed techniques suffers less from stagnation than the best known ant algorithms of ACS and MMAS.