Abstract
Swarming behaviour has been the subject of extensive objective analysis since Reynolds introduced the first-computer based ‘boids’ model in 1987. The current study extends that work by applying a range of measures — comprising existing and novel ‘group’ and ‘order’ measures and measures originally applied to chaotic systems — to a simplified version of Reynolds’ original boids model. Classifier models are then developed to identify preferred measures and combinations of measures that can accurately identify and quantify swarming behaviour. Novel combinations of existing measures show promise in providing a single measure to identify and quantify swarming. The results also suggest that there may be degrees of ‘swarminess’ rather than a simple swarming/not-swarming situation. Better understanding of swarming systems, including identifying measures that can define the parameter space in which swarming occurs and quantifying the resulting swarming dynamics, is potentially useful in a range of applications from targeted use of swarm intelligence systems to developing realistic graphics for visual effects. The predictive models developed in this study can be used as a basis for improving the predictability and tuning the behaviour of swarming systems.
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Harvey, J., Merrick, K., Abbass, H. (2016). Quantifying Swarming Behaviour. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_12
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DOI: https://doi.org/10.1007/978-3-319-41000-5_12
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