Movatterモバイル変換


[0]ホーム

URL:


Skip to main content

Advertisement

Springer Nature Link
Log in

Bacterial Colony Optimization: Principles and Foundations

  • Conference paper

Part of the book series:Communications in Computer and Information Science ((CCIS,volume 304))

Included in the following conference series:

  • 2396Accesses

Abstract

In this paper we proposes a new optimization algorithm—Bacterial Colony Optimization (BCO) which formulates the bacterial behavior model in a new way. The model is based on the principle of artificial bacterial behavior, including Chemotaxis, Communication, Elimination, Reproduction and Migration. The Chemotaxis and Communication are spread over the whole optimization process while other behaviors are implemented only when their relevant conditions are reached. Experiment results have proved a high efficiency searching capability of the new proposed artificial bacterial colony.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Tan, Q., He, Q., Zhao, W.Z.: An Improved FCMBP Fuzzy Clustering Method Based on Evolutionary Programming. Computers & Mathematics with Applications 6(4), 1129–1144 (2010)

    MathSciNet  Google Scholar 

  2. Vasconcelos, J.A., Ramirez, J.A., Takahashi, R.H.C., Saldanha, R.R.: Improvements in Genetic Algorithms. IEEE Transactions on Magnetics 37(5), 3414–3417 (2001)

    Article  Google Scholar 

  3. Akbari, R., Ziarati, K.: A Multilevel Evolutionary Algorithm for Optimizing Numerical Functions. International Journal of Industrial Engineering Computations 2, 419–430 (2011)

    Article  Google Scholar 

  4. Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of International Conference on Neural Networks, vol. 4(3), pp. 1942–1948 (1995)

    Google Scholar 

  5. Karaboga, D., Akay, B.: A Comparative Study of Artificial Bee Colony Algorithm. Applied Mathematics and Computation 214, 108–132 (2009)

    Article MathSciNet MATH  Google Scholar 

  6. Dorigo, M., Birattari, M., Stutzle, T.: Ant Colony Optimization. Computational Intelligence Magazine 1(4), 28–39 (2006)

    Google Scholar 

  7. Passino, K.M.: Biomimicry of Bacterial Foraging for Distributed Optimization and Control. IEEE Control Systems Magazine 22(3), 52–67 (2002)

    Article MathSciNet  Google Scholar 

  8. Muller, S.D., Marchetto, J., Airaghi, S., Koumoutsakos, P.: Optimization Based on Bacterial Chemotaxis. IEEE Transactions on Evolutionary Computation 6(1), 16–30 (2002)

    Article  Google Scholar 

  9. Chu, Y., Mi, H., Liao, H.L., Zhen, J., Wu, Q.H.: A Fast Bacterial Swarming Algorithm for High-Dimensional Function Optimization. In: IEEE Congress on Evolutionary Computation (CEC), pp. 3135–3140 (2008)

    Google Scholar 

  10. Niu, B., Fan, Y., Wang, H.: Novel Bacterial Foraging Optimization with Time-varying Chemotaxis Step. International Joural of Artifical Intelligence 7, 257–273 (2011)

    Google Scholar 

  11. Niu, B., Wang, H., Tan, L.J., Li, L.: Improved BFO with Adaptive Chemotaxis Step for Global Optimization. In: International Conference on Computational Intelligence and Security (CIS), pp. 76–80 (2011)

    Google Scholar 

  12. Niu, B., Wang, H., Tan, L.J., Xu, J.: Multi-Objective Optimization Using BFO Algorithm. In: Huang, D.-S., Gan, Y., Premaratne, P., Han, K. (eds.) ICIC 2011. LNCS, vol. 6840, pp. 582–587. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  13. Niu, B., Xue, B., Li, L., Chai, Y.: Symbiotic Multi-swarm PSO for Portfolio Optimization. In: Huang, D.-S., Jo, K.-H., Lee, H.-H., Kang, H.-J., Bevilacqua, V. (eds.) ICIC 2009. LNCS, vol. 5755, pp. 776–784. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Management, Shenzhen University, Shenzhen, 518060, China

    Ben Niu & Hong Wang

  2. Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, China

    Ben Niu

  3. Institute for Cultural Industries, Shenzhen University, Shenzhen, 518060, China

    Ben Niu

Authors
  1. Ben Niu

    You can also search for this author inPubMed Google Scholar

  2. Hong Wang

    You can also search for this author inPubMed Google Scholar

Editor information

Editors and Affiliations

  1. Machine Learning and Systems Biology Laboratory, School of Electronics and Information Engineering, Tongji University, Shanghai, China

    De-Shuang Huang

  2. Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, India

    Phalguni Gupta

  3. Department of Chemistry, University of Louisville, 2320 South Brook Street, 40292, Louisville, Kentucky, USA

    Xiang Zhang

  4. School of Electrical, Computer & Telecommunications Engineering, The University of Wollongong,, 2522, North Wollongong, NSW, Australia

    Prashan Premaratne

Rights and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Niu, B., Wang, H. (2012). Bacterial Colony Optimization: Principles and Foundations. In: Huang, DS., Gupta, P., Zhang, X., Premaratne, P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31837-5_73

Download citation

Publish with us

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • 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


[8]ページ先頭

©2009-2025 Movatter.jp