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Computational swarm intelligence consists of multiple artificial simpleagents exchanging information while exploring a search space. Despite a richliterature in the field, with works improving old approaches and proposing newones, the mechanism by which complex behavior emerges in these systems is stillnot well understood. This literature gap hinders the researchers' ability todeal with known problems in swarms intelligence such as premature convergence,and the balance of coordination and diversity among agents. Recent advances inthe literature, however, have proposed to study these systems via the networkthat emerges from the social interactions within the swarm (i.e., theinteraction network). In our work, we propose a definition of the interactionnetwork for the Artificial Bee Colony (ABC) algorithm. With our approach, wecaptured striking idiosyncrasies of the algorithm. We uncovered the differentpatterns of social interactions that emerge from each type of bee, revealingthe importance of the bees variations throughout the iterations of thealgorithm. We found that ABC exhibits a dynamic information flow through theuse of different bees but lacks continuous coordination between the agents.

Characterizing the Social Interactions in the Artificial Bee Colony Algorithm