1 research outputs found
Characterizing the Social Interactions in the Artificial Bee Colony Algorithm
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.Comment: 9 pages, 10 figure