Genetic Algorithms for Optimization of Boids Model (original) (raw)

Abstract

In this paper, we present an extended boids model for simulating the aggregate moving of fish schools in a complex environment. Three behavior rules are added to the extended boids model: following a feed; avoiding obstacle; avoiding enemy boids. The moving vector is a linear combination of every behavior rule vector, and the coefficients should be optimized. We also proposed a genetic algorithm to optimize the coefficients. Experimental results show that by using the GA-based optimization, the aggregate motions of fish schools become more realistic and similar to behaviors of real fish world.

Preview

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Reynolds, C.W.: Flocks, Herds, and Schools: A Distributed Behavioral Model. Computer Graphics 21, 25–34 (1987)
    Article Google Scholar
  2. DeAngelis, D.L., Shuter, B.J., Ridgeway, M.S., Blanchfield, P., Friesen, T., Morgan, G.E.: Modeling early life-history stages of smallmouth bass in Ontario lakes. Transaction of the American Fisheries Society, 9–11 (1991)
    Google Scholar
  3. Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
    MATH Google Scholar
  4. Forrest, S.: Genetic algorithms: principles of natural selection applied to computation. Science 261, 872–878 (1993)
    Article Google Scholar
  5. Chen, Y.W., Nakao, Z., Arakaki, K., Fang, X., Tamura, S.: Restoration of Gray Images Based on a Genetic Algorithm with Laplacian Constraint. Fuzzy Sets and Systems 103, 285–293 (1999)
    Article Google Scholar
  6. Chen, Y.W., Nakao, Z., Arakaki, K., Tamura, S.: Blind Deconvolution Based on Genetic Algorithms. IEICE Trans. Fundamentals E-80-A, 2603–2607 (1997)
    Google Scholar
  7. Mendoza, N., Chen, Y.W., Nakao, Z., Adachi, T.: A hybrid optimization method using real-coded multi-parent EA, simplex and simulated annealing with applications in the resolution of overlapped signals. Applied Soft Computing 1, 225–235 (2001)
    Article Google Scholar
  8. Kobayashi, K.: Interactive fish school generation system using GA. Graduation thesis of Ritsumeikan Univ. (2005)
    Google Scholar
  9. Shreiner, D.: Open GL Reference Manual: The Official Reference Document to Open Gl, Version 1.4. Addison-Wesley, Reading (2004)
    Google Scholar

Download references

Author information

Authors and Affiliations

  1. School of Information Science and Eng., Ristumeikan Univ., Shiga, 525-8577, Japan
    Yen-Wei Chen & Kanami Kobayashi
  2. College of Elect. and Information Eng., Central South Forest Univ., Changsha, 410004, China
    Yen-Wei Chen
  3. School of Education, Soochow University, Suzhou, Jiangsu, 215006, China
    Xinyin Huang
  4. Faculy of Eng., Univ. of the Ryukyus, Okinawa, 903-0213, Japan
    Zensho Nakao

Authors

  1. Yen-Wei Chen
  2. Kanami Kobayashi
  3. Xinyin Huang
  4. Zensho Nakao

Editor information

Editors and Affiliations

  1. School of Design, Engineering and Computing, Bournemouth University, UK
    Bogdan Gabrys
  2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK
    Robert J. Howlett
  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, SA, 5095, Mawson Lakes, Australia
    Lakhmi C. Jain

Rights and permissions

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chen, YW., Kobayashi, K., Huang, X., Nakao, Z. (2006). Genetic Algorithms for Optimization of Boids Model. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893004\_7

Download citation

Keywords

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Publish with us