Characterizing the Social Interactions in the Artificial Bee Colony Algorithm

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.

Overview on Binary Optimization Using Swarm-Inspired Algorithms

“…Some works argue that evaluating the social interaction of simple reactive agents. We can understand the peculiarities of the swarm-based algorithm and probably understand the differences between different versions, rules, and operators [239]- [244]. Besides, the extensive use of benchmark functions to evaluate the search capability of the methods does not help the field to determine the best models to deal with real-world problems.…”

Search trajectory networks: A tool for analysing and visualising the behaviour of metaheuristics

“…The idea, as initially proposed by Zelinka and Davendra [20], is to use graphs whose connections represent interactions amongst the individuals during all generations; vertices are individuals that are activated by other individuals, incrementally from generation to generation. Follow-up work has studied differential evolution [21,22] and particle swarm optimisation methods using this approach [23,24], where the emphasis is to model the communication or influence of individuals (or particles) inside the population or swarm. These network models have been shown to be useful for visualising the behaviour and capturing the trade-off between exploration and exploitation of the studied algorithms.…”