Skip to main content
SpringerLink
Log in

Distributed Perception Algorithm

  • Conference paper
  • First Online:
Advances in Swarm Intelligence (ICSI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9713))

Included in the following conference series:

  • 1805 Accesses

Abstract

In this paper we describe the Distributed Perception Algorithm (DPA) which is partly inspired by the schooling behaviour of ‘golden shiner’ fish (Notemigonus crysoleucas). These fish display a preference for shaded habitat and recent experimental work has shown that the fish use both individual and distributed perception in navigating their environment. We assess the contribution of each element of the DPA and also benchmark its results against those of canonical PSO.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bastos Filho, C., de Lima Neto, F., Lins, A., Nascimento, A., Lima, M.: A novel search algorithm based on fish school behavior. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 2646–2651. IEEE Press (2008)

    Google Scholar 

  2. Bastos Filho, C., de Lima Neto, F., Sousa, M., Pontes, M., Madeiro, S.: On the influence of the swimming operators in the fish school search algorithm. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 5012–5017. IEEE Press (2009)

    Google Scholar 

  3. Berdahl, A., Torney, C., Ioannou, C., Faria, J., Couzin, I.: Emergent sensing of complex environments by mobile animal groups. Science 339, 574–576 (2013)

    Article  Google Scholar 

  4. Brabazon, A., Cui, W., O’Neill, M.: Information propagation in a social network: the case of the fish algorithm. In: Krol, D., Fay, D., Gabrys, B. (eds.) Propagation Phenomena in Real World Networks, vol. 85, pp. 27–51. Springer, Switzerland (2015)

    Google Scholar 

  5. Brabazon, A., O’Neill, M., McGarraghy, S.: Natural Computing Algorithms. Springer, Heidelberg (2015)

    Book  MATH  Google Scholar 

  6. Dorigo, M.: Optimization, Learning and Natural Algorithms, Ph.D. Thesis, Politecnico di Milano (1992)

    Google Scholar 

  7. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B Cybern. 26, 29–41 (1996)

    Article  Google Scholar 

  8. Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)

    MATH  Google Scholar 

  9. Engelbrecht, A.: Fundamentals of Computational Swarm Intelligence. Wiley, Chichester (2005)

    Google Scholar 

  10. Grunbaum, D., Viscido, S., Parrish, J.: Extracting interative control algorithms from group dynamics of schooling fish. In: Kumar, V., Leonard, N., Morse, A.S. (eds.) Cooperative Control. LNCIS, vol. 309, pp. 103–117. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  11. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Press (1995)

    Google Scholar 

  12. Kennedy, J., Eberhart, R., Shi, Y.: Swarm Intelligence. Morgan Kaufman, San Mateo (2001)

    Google Scholar 

  13. Li, X., Shao, Z., Qian, J.: An optimizing method based on autonomous animats: fish swarm algorithm. Syst. Eng. Theor. Pract. 22, 32–38 (2002)

    Google Scholar 

  14. Passino, K.: Distributed optimization and control using only a germ of intelligence. In: Proceedings of the 2000 IEEE International Symposium on Intelligent Control, pp. 5–13. IEEE Press (2000)

    Google Scholar 

  15. Passino, K.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22, 52–67 (2002)

    Article  Google Scholar 

  16. Pham, D., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M.: The Bees Algorithm - a novel tool for complex optimisation problems. In: Proceedings of International Production Machines and Systems (IPROMS), pp. 454–459 (2006)

    Google Scholar 

  17. Reebs, S.: Can a minority of informed leaders determine the foraging movements of a fish shoal? Anim. Behav. 59, 403–409 (2000)

    Article  Google Scholar 

  18. Strandburg-Peshkin, A., Twomey, C., Bode, N., Kao, A., Katz, Y., Ioannou, C., Rosenthal, S., Torney, C., Wu, H., Levin, S., Couzin, I.: Visual sensory networks and effective information transfer in animal groups. Curr. Biol. 23(17), R709–R711 (2013)

    Article  Google Scholar 

Download references

Acknowledgement

This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant Number 08/SRC/FM1389.

Author information

Authors and Affiliations

  1. Natural Computing Research and Applications Group, School of Business, University College Dublin, Dublin, Ireland

    Anthony Brabazon & Wei Cui

Authors
  1. Anthony Brabazon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anthony Brabazon .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzen University, Shenzhen, China

    Li Li

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Brabazon, A., Cui, W. (2016). Distributed Perception Algorithm. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-41009-8_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41008-1

  • Online ISBN: 978-3-319-41009-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation