On the Exploration and Exploitation Capabilities of the Artificial Bee Colony Algorithm

In this paper, we investigate the exploration and exploitation capabilities of the Artificial Bee Colony (ABC) algorithm using novel attraction basin-based measures. Previous claims about the ABC’s weak exploitation and exploration capabilities have been scrutinized. These claims are not based on exploration and exploitation measurements and, as such, are questionable. Direct measurements are needed to get real insights into the exploration and exploitation capabilities of any search algorithm. The results show that indirect measurements based on diversity are not appropriate. Our newly developed attraction basin-based measurements allow us to differentiate between exploration types (successful, failed, deceptive, successful rejection) and exploitation types (successful, unsuccessful). Namely, it is not only important that an algorithm is in the exploration phase, but also that promising regions with better solutions are not abandoned and that regions with worse solutions are visited less frequently. Similarly, during the exploitation phase, it is important to discover better solutions in the neighborhood and not exploit in an unsuccessful direction. It has been shown that ABC’s exploration and exploitation capabilities are versatile, and can adapt to different fitness landscapes successfully.