Towards flexible swarms: comparison of flocking models with varying complexity

Abstract One remarkable feat of biological swarms is their ability to work under very different environmental circumstances and disturbances. They exhibit a flexible kind of robustness, which accommodates external events without staying on rigid positions. Based on the observation that conventionally robust flocking models can be very complex and use information unavailable to biological swarm, we undertook a wide investigation into the properties of existing flocking models such as Boid, Couzin, Vicsek, and Cucker–Smale. That is, to see if a similar “natural” flexibility could be observed in flocking models with lower complexity. We established a toolset of three metrics which allows for a comprehensive evaluation of different flocking models. These metrics measure general model performance, robustness under noise, as well as a naive complexity of the model itself. Our results show a general trend for divergence between performance and robustness. The most robust models had a medium–high complexity. While our results show no clear relation between robustness and low complexity, we discuss examples for robust behavior with simple rules.