Flocking and swarming in a multi-agent dynamical system
Abstract: Over the past few decades, the research community has been interested in the study of multi-agent systems and their emerging collective dynamics. These systems are all around us in nature, like bacterial colonies, fish schools, bird flocks, as well as in technology, such as microswimmers and robotics, to name a few. Flocking and swarming are two key components of the collective behaviours of multi-agent systems. In flocking, the agents coordinate their direction of motion, but in swarming, they congregate in space to organise their spatial position. We investigate a minimal mathematical model of locally interacting multi-agent system where the agents simultaneously swarm in space and exhibit flocking behaviour. Various cluster structures are found, depending on the interaction range. When the coupling strength value exceeds a crucial threshold, flocking behaviour is observed. We do in-depth simulations and report the findings by changing the other parameters and with the incorporation of noise.
- W. Bialek, A. Cavagna, I. Giardina, T. Mora, E. Silvestri, M. Viale, and A. M. Walczak, “Statistical mechanics for natural flocks of birds,” Proceedings of the National Academy of Sciences 109, 4786–4791 (2012).
- C. K. Hemelrijk and H. Hildenbrandt, “Schools of fish and flocks of birds: their shape and internal structure by self-organization,” Interface Focus 2, 726–737 (2012).
- D. J. Sumpter, Collective animal behavior (Princeton University Press, 2010).
- C. W. Reynolds, “Flocks, herds and schools: A distributed behavioral model,” in Proceedings of the 14th annual conference on Computer graphics and interactive techniques (1987) pp. 25–34.
- J. Toner and Y. Tu, “Flocks, herds, and schools: A quantitative theory of flocking,” Physical Review E 58, 4828 (1998).
- A. Okubo, “Dynamical aspects of animal grouping: swarms, schools, flocks, and herds,” Advances in Biophysics 22, 1–94 (1986).
- K. S. Norris and C. R. Schilt, “Cooperative societies in three-dimensional space: on the origins of aggregations, flocks, and schools, with special reference to dolphins and fish,” Ethology and Sociobiology 9, 149–179 (1988).
- F. Cucker and S. Smale, “Emergent behavior in flocks,” IEEE Transactions on Automatic Control 52, 852–862 (2007).
- J. Toner, Y. Tu, and S. Ramaswamy, “Hydrodynamics and phases of flocks,” Annals of Physics 318, 170–244 (2005).
- M. A. Al-Betar, M. A. Awadallah, H. Faris, X.-S. Yang, A. T. Khader, and O. A. Alomari, “Bat-inspired algorithms with natural selection mechanisms for global optimization,” Neurocomputing 273, 448–465 (2018).
- A. Shklarsh, G. Ariel, E. Schneidman, and E. Ben-Jacob, “Smart swarms of bacteria-inspired agents with performance adaptable interactions,” PLoS Computational Biology 7, e1002177 (2011).
- W. Van der Hoek and M. Wooldridge, “Multi-agent systems,” Foundations of Artificial Intelligence 3, 887–928 (2008).
- M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo, “Swarm robotics: a review from the swarm engineering perspective,” Swarm Intelligence 7, 1–41 (2013).
- J. Kennedy, “Swarm intelligence,” in Handbook of nature-inspired and innovative computing: integrating classical models with emerging technologies (Springer, 2006) pp. 187–219.
- T. Vicsek and A. Zafeiris, ‘‘Collective motion,” Physics Reports 517, 71–140 (2012).
- C. Hartman and B. Benes, “Autonomous boids,” Computer Animation and Virtual Worlds 17, 199–206 (2006).
- J. A. Carrillo, M. Fornasier, G. Toscani, and F. Vecil, “Particle, kinetic, and hydrodynamic models of swarming,” Mathematical Modeling of Collective Behavior in Socio-economic and Life Sciences , 297–336 (2010).
- T. Vicsek, A. Czirók, E. Ben-Jacob, I. Cohen, and O. Shochet, “Novel type of phase transition in a system of self-driven particles,” Physical Review Letters 75, 1226 (1995).
- A. T. Winfree, “Biological rhythms and the behavior of populations of coupled oscillators,” Journal of Theoretical Biology 16, 15–42 (1967).
- A. Pikovsky, M. Rosenblum, J. Kurths, and A. Synchronization, “A universal concept in nonlinear sciences,” Self 2, 3 (2001).
- J. Buck, “Synchronous rhythmic flashing of fireflies. ii.” The Quarterly Review of Biology 63, 265–289 (1988).
- Z. Néda, E. Ravasz, Y. Brechet, T. Vicsek, and A.-L. Barabási, “The sound of many hands clapping,” Nature 403, 849–850 (2000).
- T. Womelsdorf and P. Fries, “The role of neuronal synchronization in selective attention,” Current Opinion in Neurobiology 17, 154–160 (2007).
- C. Schäfer, M. G. Rosenblum, J. Kurths, and H.-H. Abel, “Heartbeat synchronized with ventilation,” Nature 392, 239–240 (1998).
- M. Rohden, A. Sorge, M. Timme, and D. Witthaut, “Self-organized synchronization in decentralized power grids,” Physical Review Letters 109, 064101 (2012).
- M. L. Sichitiu and C. Veerarittiphan, “Simple, accurate time synchronization for wireless sensor networks,” in 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003., Vol. 2 (IEEE, 2003) pp. 1266–1273.
- J. D. Little, “The synchronization of traffic signals by mixed-integer linear programming,” Operations Research 14, 568–594 (1966).
- A. J. Bernoff and C. M. Topaz, “Nonlocal aggregation models: A primer of swarm equilibria,” Siam REVIEW 55, 709–747 (2013).
- C. M. Topaz, A. L. Bertozzi, and M. A. Lewis, “A nonlocal continuum model for biological aggregation,” Bulletin of Mathematical Biology 68, 1601–1623 (2006).
- C. M. Topaz and A. L. Bertozzi, “Swarming patterns in a two-dimensional kinematic model for biological groups,” SIAM Journal on Applied Mathematics 65, 152–174 (2004).
- M. Frasca, A. Buscarino, A. Rizzo, L. Fortuna, and S. Boccaletti, “Synchronization of moving chaotic agents,” Physical Review Letters 100, 044102 (2008).
- S. N. Chowdhury, S. Majhi, M. Ozer, D. Ghosh, and M. Perc, “Synchronization to extreme events in moving agents,” New Journal of Physics 21, 073048 (2019).
- S. Majhi, D. Ghosh, and J. Kurths, “Emergence of synchronization in multiplex networks of mobile rössler oscillators,” Physical Review E 99, 012308 (2019).
- K. P. O’Keeffe, H. Hong, and S. H. Strogatz, “Oscillators that sync and swarm,” Nature Communications 8, 1504 (2017).
- G. K. K. Sar and D. Ghosh, ‘‘Dynamics of swarmalators: A pedagogical review,” Europhysics Letters (2022).
- G. K. Sar, D. Ghosh, and K. O’Keeffe, “Pinning in a system of swarmalators,” Physical Review E 107, 024215 (2023a).
- G. K. Sar, S. N. Chowdhury, M. Perc, and D. Ghosh, “Swarmalators under competitive time-varying phase interactions,” New Journal of Physics 24, 043004 (2022).
- G. K. Sar, D. Ghosh, and K. O’Keeffe, “Solvable model of driven matter with pinning,” arXiv preprint arXiv:2306.09589 (2023b).
- Y. Chen and T. Kolokolnikov, “A minimal model of predator–swarm interactions,” Journal of The Royal Society Interface 11, 20131208 (2014).
- T. Kolokolnikov, J. A. Carrillo, A. Bertozzi, R. Fetecau, and M. Lewis, “Emergent behaviour in multi-particle systems with non-local interactions,” (2013).
- Z. Liu and L. Guo, “Synchronization of multi-agent systems without connectivity assumptions,” Automatica 45, 2744–2753 (2009).
- A. M. Reynolds, G. E. McIvor, A. Thornton, P. Yang, and N. T. Ouellette, “Stochastic modelling of bird flocks: accounting for the cohesiveness of collective motion,” Journal of the Royal Society Interface 19, 20210745 (2022).
- A. M. Reynolds, “Stochasticity may generate coherent motion in bird flocks,” Physical Biology 20, 025002 (2023).
- A. M. Reynolds and N. T. Ouellette, “Swarm formation as backward diffusion,” Physical Biology 20, 026002 (2023).
- S. M. Ahn and S.-Y. Ha, “Stochastic flocking dynamics of the cucker–smale model with multiplicative white noises,” Journal of Mathematical Physics 51 (2010).
- A. Morin, J.-B. Caussin, C. Eloy, and D. Bartolo, “Collective motion with anticipation: Flocking, spinning, and swarming,” Physical Review E 91, 012134 (2015).
- D. H. Kelley and N. T. Ouellette, “Emergent dynamics of laboratory insect swarms,” Scientific Reports 3, 1073 (2013).
- I. Aihara, H. Kitahata, K. Yoshikawa, and K. Aihara, “Mathematical modeling of frogs’ calling behavior and its possible application to artificial life and robotics,” Artificial Life and Robotics 12, 29–32 (2008).
- I. Aihara, T. Mizumoto, T. Otsuka, H. Awano, K. Nagira, H. G. Okuno, and K. Aihara, “Spatio-temporal dynamics in collective frog choruses examined by mathematical modeling and field observations,” Scientific Reports 4, 3891 (2014).
- R. C. Fetecau, Y. Huang, and T. Kolokolnikov, “Swarm dynamics and equilibria for a nonlocal aggregation model,” Nonlinearity 24, 2681 (2011).
- J. Zhu, J. Lu, and X. Yu, “Flocking of multi-agent non-holonomic systems with proximity graphs,” IEEE Transactions on Circuits and Systems I: Regular Papers 60, 199–210 (2012).
- Z. Liu and L. Guo, “Connectivity and synchronization of vicsek model,” Science in China Series F: Information Sciences 51, 848–858 (2008).
- R. Olfati-Saber, “Flocking for multi-agent dynamic systems: Algorithms and theory,” IEEE Transactions on automatic control 51, 401–420 (2006).
- R. Olfati-Saber, J. A. Fax, and R. M. Murray, “Consensus and cooperation in networked multi-agent systems,” Proceedings of the IEEE 95, 215–233 (2007).
- R. Olfati-Saber and R. M. Murray, “Consensus problems in networks of agents with switching topology and time-delays,” IEEE Transactions on Automatic Control 49, 1520–1533 (2004).
- A. Jadbabaie, J. Lin, and A. S. Morse, “Coordination of groups of mobile autonomous agents using nearest neighbor rules,” IEEE Transactions on Automatic Control 48, 988–1001 (2003).
- M. M. Zavlanos, A. Jadbabaie, and G. J. Pappas, “Flocking while preserving network connectivity,” in 2007 46th IEEE Conference on Decision and Control (IEEE, 2007) pp. 2919–2924.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.