Modeling and Path Generation Approaches for Crowd Simulation Based on Computational Intelligence[J]. Chinese Journal of Electronics, 2012, 21(4): 636-641.
Citation: Modeling and Path Generation Approaches for Crowd Simulation Based on Computational Intelligence[J]. Chinese Journal of Electronics, 2012, 21(4): 636-641.

Modeling and Path Generation Approaches for Crowd Simulation Based on Computational Intelligence

  • Received Date: 2011-04-01
  • Rev Recd Date: 2011-11-01
  • Publish Date: 2012-10-25
  • Modeling and simulating group behaviours have been an active research topic in the field of computer animation and game. This paper presents some novel approaches for supporting entity modeling and path generation in crowd simulation. It analyses related work about crowd simulation first. Then, an entity modeling approach based on CGA (Cellular genetic algorithm) and NURBS (Non uniform relational B splines) technologies is presented. Next, following the analysis to PSO (Particle swarm optimization) and ABC (Artificial bee colony) algorithms, a crowd path generative approach based on ABCPSO is put forward. After that, a simulating example of crowd cohesion and performance comparison are exhibited for showing the efficiency of the algorithms. Finally, the current work is summarized and an outlook for the future work is given.
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  • A.M. Turing, “The chemical basis of morphogenesis”, PhilosophicalTransactions of the Royal Society of London, SeriesB, Biological Sciences, Vol.237, No.641. pp.37-72, 1952.
    S. Johnson, Emergence: the connected lives of ants, brains,cities, and software”, Scribner NewYork, 2002.
    C. Reynolds, “Flocks, birds, and schools, A distributed behavioralmodel”, Computer Graphics, Vol.21, No.4, pp.25-34, 1987.
    X. Tu, D. Terzopoulos, “Artificial fishes: physics, locomotion,perception, behavior”, Proceedings of SIGGRAPH 1994, NY,USA, pp.43-50, 1994.
    C.R. Ward, F. Gobet, G. Kendall, “Evolving collective behaviorin an artificial ecology”, Artificial Life, Vol.7, No.2, pp.191-209,2001.
    V.J. Blue, J.L. Adler, “Cellular automata microsimulation formodelling bi-directional pedestrian walkways”, TransportationResearch, Part B: Methodological, Vol.35, No.3, pp.293-312,2001.
    X.J. Ban, D.P. Jiang, S.R. Ning and Y.X. Yi, “Research on behaviorroute selection from a cognitive prospective in computeranimation”, Acta Electronica Sinica, Vol.37, No.4, pp.758-763,2009.
    F. Qiu, X. Hu, “Modelling group structures in pedestrian crowdsimulation”, Simulation Modelling Practice and Theory, Vol.18,No.1, pp.190-205, 2010.
    Y.P. Chen, Y.Y. Lin, “Controlling the movement of crowds incomputer graphics by using the mechanism of particle swarmoptimization”, Applied Soft Computing, Vol.9, pp.1170-1176,2009.
    N. Fridman, G.A. Kaminka, “Towards a computational modelof social comparison: Some implications for the cognitive architecture”,Cognitive Systems Research, Vol.12, pp.186-197,2011.
    T. Ceccherini-Silberstein, M. Coornaert, Cellular Automata andGroups, Springer, 2010.
    J. Kennedy, R.C. Eberhart, “Particle swarm optimization”,Proceedings of the IEEE International Conference on NeuralNetworks IV, Vol.4, IEEE Press, Piscataway, NJ., pp.1942-1948, 1995.
    D. Karaboga, “An idea based on honey bee swarm for numericaloptimization”, Technical Report TR06, Erciyes University,Engineering Faculty, Computer Engineering Department, 2005.
    H. Liu, S. Xu, “Group animation path generation based on particleswarm optimisation”, Proceedings of the 14th InternationalConference on Computer Supported Cooperative Work in Design,Fudan University, Shanghai, China, pp.37-42, 2010.
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