# Characterizing the Social Interactions in the Artificial Bee Colony Algorithm

## TL;DR

This paper investigates the social interaction networks within the Artificial Bee Colony algorithm, revealing dynamic information flow and the importance of bee variations, which enhances understanding of swarm behavior and coordination.

## Contribution

It introduces a novel interaction network definition for ABC, uncovering unique social patterns and the role of bee variations in the algorithm's dynamics.

## Key findings

- ABC exhibits dynamic information flow among bees.
- The algorithm lacks continuous coordination between agents.
- Different bee types influence social interaction patterns.

## Abstract

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.