Speaker
Description
The capacity of groups to accomplish complicated tasks that exceed individual skills is acknowledged in natural and human environments [1]. Animals that live in groups demonstrate complex collective behaviours that allows to fulfil fundamental biologic functionalities, such as foraging or defence from predators. In this connection, the collective intelligence of animal swarms can by mimicked by artificial systems, made of physical or virtual agents, to achieve superior performance in optimization problems [2].
Merging ingredients from meta-heuristic optimization approaches and consensus-driven methods, we introduce a collective intelligence model for the cooperation of a vehicle swarm. The model is formulated as an overdamped Langevin equation, with minor tuning parameters. The model, governing the balance between social interactions, cognitive stimuli and stochastic fluctuations leads the swarm to accomplish complex tasks, such as the optimization of multimodal functions.
The effectiveness of the model as optimization tool is tested against several static landscape functions, as well as in a simulated marine environment, where a small swarm of underwater vehicles are required to localize a pollutant source in the open sea.
REFERENCES
[1] Couzin, I. D. Collective cognition in animal groups. Trends in cognitive sciences
13, 36–43 (2009).
[2] Hassanien, A. E. & Emary, E. Swarm intelligence: principles, advances, and
applications (CRC press, 2018).
