Swarm collective behaviour driven by artificial physics

What kind of interaction and collaborative law among social creatures gives rise to swarm intelligence behaviour? Humans have cognitive limitation and therefore lack understanding of the essence of swarm intelligence. This leads to the emergence of the internal mechanism of swarm intelligence behaviour. In this paper, an artificial physics method to construct the swarm model is introduced. In the model, each individual is regarded as a physical particle. There exists virtual force among individuals, and each individual repels other individuals that are closer than R, while attracting individuals that are farther than R in distance. The law of force between any two individuals is defined and the potential field existence of the model is proved. The influence of the different values of the parameter p to swarm collective behaviour is analysed by drawing the force law curve. The simulation examples illustrate the effectiveness of the swarm model.

[1]  K. Warburton,et al.  Tendency-distance models of social cohesion in animal groups. , 1991, Journal of Theoretical Biology.

[2]  A. Mogilner,et al.  A non-local model for a swarm , 1999 .

[3]  D. Morale,et al.  Modeling and simulating animal grouping : Individual-based models , 2001, Future Gener. Comput. Syst..

[4]  William M. Spears,et al.  Two Formal Gas Models for Multi-agent Sweeping and Obstacle Avoidance , 2004, FAABS.

[5]  William M. Spears,et al.  Distributed, Physics-Based Control of Swarms of Vehicles , 2004 .

[6]  Liping Xie,et al.  Convergence analysis and performance of the extended artificial physics optimization algorithm , 2011, Appl. Math. Comput..

[7]  Diana SPEARS,et al.  Physics-Based Robot Swarms For Coverage Problems , .

[8]  A. Ōkubo,et al.  Di?usion and ecological problems: mathematical models , 1980 .

[9]  Eric Klopfer,et al.  Technologies to support the creation of complex systems models--using StarLogo software with students. , 2003, Bio Systems.

[10]  Ying Tan,et al.  Artificial physics optimisation: a brief survey , 2010, Int. J. Bio Inspired Comput..

[11]  Herbert Levine,et al.  Cooperative self-organization of microorganisms , 2000 .

[12]  K.M. Passino,et al.  Stability analysis of social foraging swarms , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  C. Breder.  Equations Descriptive of Fish Schools and Other Animal Aggregations , 1954 .

[14]  明 大久保,et al.  Diffusion and ecological problems : mathematical models , 1980 .

[15]  Suranga Hettiarachchi,et al.  An Overview of Physicomimetics , 2004, Swarm Robotics.