Explaining Coordination in Swarm Intelligence using Network Science, Computer Science – PhD (Funded)

Supervisors

Professor Ronaldo Menezes, University of Exeter
Professor Ed Keedwell, University of Exeter

The University of Exeter’s College of Engineering, Mathematics and Physical Sciences, is inviting applications for a fully-funded PhD studentship to commence in September 2019 or as soon as possible thereafter. The studentship will cover UK/EU/International tuition fees plus an annual tax-free stipend of at least £14,777 for 3.5 years full-time, or pro rata for part-time study. The student would be based in Computer Science in the College of Engineering, Mathematics and Physical Sciences at the Streatham Campus in Exeter.

Computational swarm intelligence consists of multiple artificial simple agents exchanging information while exploring a search space. Despite the rich literature in the field, with works improving old approaches and proposing new ones, the mechanism by which complex behaviour emerges in these systems is still not well understood. This literature gap hinders 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).

The concept of an interaction network, allows researchers to (i) understand swarm systems from a mezzo level in which the specific inspiration (bees, ants, bats, etc.) are disregarded in favour of an approach that allows us to understand better how and why these system achieve success, and (ii) provide an organization of such swarm approaches, thus enabling us to classify the approaches that exist and have demonstrable mechanisms to show that one particular approach differs from another. This project aims two-fold: (1) provide a meta-classification of swarm systems using the idea of interaction networks and (2) use the interaction network framework to understand swarm approaches at a deeper level; we believe that the effect of the choice of parameters in swarm systems can be observed clearly from the interaction networks.