Characterizing the Social Interactions in the Artificial Bee Colony Algorithm
This work addresses a literature gap for researchers in computational swarm intelligence, though it is incremental as it builds on existing network-based approaches.
The authors tackled the problem of understanding complex behavior emergence in swarm intelligence by defining an interaction network for the Artificial Bee Colony (ABC) algorithm, revealing distinct social interaction patterns for different bee types and identifying a dynamic information flow but lack of continuous coordination.
Computational swarm intelligence consists of multiple artificial simple agents exchanging information while exploring a search space. Despite a rich literature in the field, with works improving old approaches and proposing new ones, the mechanism by which complex behavior emerges in these systems is still not well understood. This literature gap hinders the researchers' ability to deal with known problems in swarms intelligence such as premature convergence, and the balance of coordination and diversity among agents. Recent advances in the literature, however, have proposed to study these systems via the network that emerges from the social interactions within the swarm (i.e., the interaction network). In our work, we propose a definition of the interaction network for the Artificial Bee Colony (ABC) algorithm. With our approach, we captured striking idiosyncrasies of the algorithm. We uncovered the different patterns of social interactions that emerge from each type of bee, revealing the importance of the bees variations throughout the iterations of the algorithm. We found that ABC exhibits a dynamic information flow through the use of different bees but lacks continuous coordination between the agents.