A GP Based Two-Layer Framework for Data-Driven Modeling of Swarm Self-Organizing Rules
Tao Wang, Xingguang Peng, Yapei Wu, Jian Gao · 2019
There are many swarms of creatures in nature, which lead to a lot of highly ordered and beautiful "emergence" behaviors. For the modeling of self-organizing rules, most of the existing literature focuses on modeling according to special knowledge of physics or biology. Some self-organizing models are proposed in the literature which has been validated by the reproduction of certain "emergence" motion pattern, such as torus, or flocking. However, there are few studies about datadriven modeling of self-organizing rules of swarms. In this paper, we propose a prior knowledge free (i.e., data-driven) approach to learn the self-organizing rules of moving swarms. We use a Genetic Programming (GP) based two-layer framework to optimize the self-organizing model which is consist of neighbor selection rules and corresponding reaction rules. The proposed data-driven modeling method is validated by modeling of three typical collective behaviors (highly parallel group, dynamic parallel group and torus swarm behavior) only according to the simulation data generated from Vicsek and Couzin models. An analysis is conducted with expression tree simplification, swarm behavior reproduction and global metric evaluation. Results show that the proposed method can learn classic self-organizing rules effectively.