Fish School

Introduction

Social aggregations of autonomous individuals are a common behavior which can be observed at different scales and levels of complexity. This type of behavior occurs in a wide organisms range, from unicellular (bacteria, myxobacteria, amoeboid) to higher vertebrates (tunas, dolphins, whales). In this type of system, each individual autonomously reacts to stimuli coming from other individuals and/or the environment (usually within a perception range).

These individual-level decisions are spread through the system provoking emergent phenomena. Depending on the species, these association may be called as herds, shoals, flocks, schools or swarms. Furthermore, collective behaviors can be seen in many research areas such as: ecology and biology, sociology, physics, health care, etc.

There are two approaches in order to model population dynamics: equation-oriented or individual-oriented modeling. In equation-oriented modeling the system is represented by a set of state variables and its evolution consists of solving a set of differential equations (eg Lotka-Volterra equations, also known as prey-predator equations). In individual-oriented modeling the system is represented by a set of individuals characterized by their behaviors (interaction rules) and attributes, and an environment in which interaction occurs. Nevertheless, it is very difficult to solve an individual-oriented model analytically whereby it is necessary using simulation tools in order to observe system variables through time.

We have developed methods & techniques in order to use High Performance Computing to execute a distributed individual-oriented simulation using close-to-reality and huge models obtaining excellent speedup

Researchers

Last Contributions

Software

If you are interested in our Distributed HPC simulator package (this software is based on GNU/Linux & MPI under GNU Affero GPLv3) do not hesitate to contact with us.

References

  • A. Huth, C.Wissel, The simulation of fish schools in comparison with experimental data, Ecological Modelling 75-76 (1994) pp. 135– 46.
  • R. Suppi, P. Munt, E. Luque, Using pdes to simulate individual-oriented models in ecology: A case study, International Conference on Computational Science-Part I, (2002), pp. 107–116.
  • B. Zhou, S. Zhou, Parallel simulation of group behaviors, Winter Simulation Conference, (2004), pp. 364–370.
  • J. J. Corner, G. B. Lamont, Parallel simulation of uav swarm scenarios, Winter Simulation Conference, (2004), pp. 355–363.
  • G. Vigueras, M. Lozano, J. Ordua, Workload balancing in distributed crowd simulations: the partitioning method, The Journal of Supercomputing (2009) 1–9.
  • Y. Wang, M. Lees, W. Cai, S. Zhou, M. Low, Cluster based partitioning for agent-based crowd simulations, Winter Simulation Conference, (2009), pp. 1047 –1058.
  • Y. M. Marzouk, A. F. Ghoniem, K-means clustering for optimal partitioning and dynamic load balancing of parallel hierarchical n-body simulations, Journal of Computational Physics, 207 (2005) 493–528.
  • D. M. Rao, A. Chernyakhovsky, Parallel simulation of the global epidemiology of avian influenza, Winter Simulation Conference, 2008, pp. 1583–1591.
  • S. B. Yoginath, K. S. Perumalla, Parallel vehicular trac simulation using reverse computation-based optimistic execution, Principles of Advanced and Distributed Simulation Conference (PADS08), IEEE Computer Society, (2008), pp. 33–42.
  • X. Hu, Y. Sun, Agent-based modeling and simulation of wildland fire suppression, Winter Simulation Conference, IEEE Press, (2007), pp. 1275–1283.
  • J. Kennedy, R. C. Eberhart, Swarm intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, (2001).
  • A. Huth, C. Wissel, The simulation of the movement of fish schools, Journal of Theoretical Biology 156 (3) (1992) 365 – 385.
  • R. Vabø, G. Skaret, Emerging school structures and collective dynamics in spawning herring: A simulation study, Ecological Modelling 214 (2-4) (2008) 125–140.
  • J. C. Gonzlez, C. Dalforno, R. Suppi, E. Luque, A fuzzy logic fish school model, International Conference on Computational Science, Vol. 5544 of Lecture Notes in Computer Science, (2009), pp. 13–22.
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