An Improved Artificial Fish Cluster Behavioral Model and its Simulation

Abstract:

Biological cluster behavioral models rarely discuss about group cohesion issues at present, which makes biological colony susceptible to external environmental factors and leads to the group readily partitioning into multiple small groups. In order to enhance group cohesion and improve colony effect, the paper takes artificial fish as the research object and proposes a new model on artificial fish cluster behavior. This model is improved from standard self-propelled particles model, utilizing topological distance to ensure surrounding neighbors and to restrain the cognitive level of artificial fish. Meanwhile, Sphere method based on bounding box is adopted to test collision and proposes a hybrid collision processing scheme that only applies to cluster behavior, aimed at avoiding the penetration phenomenon of artificial fish. Finally, Experiments have been conducted. Results show that this model can simulate the cluster behavior of artificial fish precisely and make the group cohesion stronger. The proposed method is a feasible solution to the cluster model problem of cohesion.

• Periodical: Applied Mechanics and Materials (Volumes 433-435)
• Pages: 770-773
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.433-435.770
• Online since: October 2013
• Authors: Hong Chun Yuan, Si Hao Huang*, Ying Chen
• Keywords: Artificial Fish, Cluster Behavioral Model, Cognitive Constraint, Collision Detection, Group Cohesion

References

[1] Craig w. Reynolds, Flocks, herds and schools: a distributed behavioral model. Computer Graphics, (1987).

[2] T. Vicsek. Universal patterns of collective motion from minimal models of flocking. Second IEEE international conference on self-adaptive and self-organizing systems, (2008).

DOI: 10.1109/saso.2008.23

[3] Couzin, I.D., Krause, J., James, R., Ruxtion, G.D., Franks, N.R. Collective memory and spatial sorting in animal groups. Theor. Biol, 2008(218): 1-11.

DOI: 10.1006/jtbi.2002.3065

[4] Graciano Dieck Kattas, Xiao-Ke Xu and Michael Small. Dynamical Modeling of Collective Bahavior from Pigeon Flight Data. Plos Computational Biology, (2012).

DOI: 10.1371/journal.pcbi.1002449

[5] En Cai. Emergence in collective Animal Behavior. (2009).

[6] Michele Ballerini, N. cola Cabibbo and Raphael Candelier. Empirical investigation of starling flocks: a benchmark study in collective animal behavior. Animal Behaviour, 2008(76): 201-215.

DOI: 10.1016/j.anbehav.2008.02.004

[7] Teng Xu, TianLu Mao, Min Shi and ZhaoQi Wang. Improved Collision Detection Algorithm for Deformable Models. Computer Simulation, 2012, 29(11): 6-10. (In Chinese).

[8] WeiWei Wen, LiJun Fan and YunFei Bai. Collision Detection Algorithm Based on the Mix of Sphere and OBB. Software, 2011, 32(5): 21-26. (In Chinese).

[9] Jun Yu. Research on Collision Detection and Avoid Algorithm of Crowd Animations. Shandong Normal University, 2012. (In Chinese).