Skip to main content
SpringerLink
Log in
Book cover

International Conference on Artificial Intelligence and Security

ICAIS 2020: Artificial Intelligence and Security pp 362–374Cite as

An Effective Bacterial Foraging Optimization Based on Conjugation and Novel Step-Size Strategies

  • Conference paper
  • First Online:

  • 1111 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12239))

Abstract

Bacterial Foraging Optimization (BFO) is a high-efficient meta-heuristic algorithm that has been widely applied to the real world. Despite outstanding computing ability, BFO algorithms can barely avoid premature convergence in computing difficult problems, which usually leads to inaccurate solutions. To improve the computing efficiency of BFO algorithms, the paper presents an improved BFO algorithm: Conjugated Novel Step-size BFO algorithm (CNS-BFO). It employs a novel step-size evolution strategy to address limitations brought by fixed step size in many BFOs. Also, the improved BFO algorithm adopts Lévy flight strategy proposed in LPBFO and the conjugation strategy proposed in BFO-CC to enhance its computing ability. Furthermore, Experiment on 24 benchmark functions are conducted to demonstrate the efficiency of the proposed CNS-BFO algorithm. The experiment results suggest that the proposed algorithm can deliver results with better quality and smaller volatility than other meta-heuristics, and hence sufficiently mitigate the limitation of premature convergence facing many meta-heuristics.

This is a preview of subscription content, 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   119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   159.99
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

Learn about institutional subscriptions

References

  1. Alhenak, L., Hosny, M.: Genetic-frog-leaping algorithm for text document clustering. Comput. Mater. Contin. 61, 1045–1074 (2019)

    Article  Google Scholar 

  2. Biswas, A., Dasgupta, S., Das, S., Abraham, A.: A synergy of differential evolution and bacterial foraging optimization for global optimization. Neural Netw. World 17(6), 607 (2007)

    Google Scholar 

  3. Dasgupta, S., Das, S., Abraham, A., Biswas, A.: Adaptive computational chemotaxis in bacterial foraging optimization: an analysis. IEEE Trans. Evol. Comput. 13(4), 919–941 (2009)

    Article  Google Scholar 

  4. Deepa, S.R., Rekha, D.: Bacterial foraging optimization-based clustering in wireless sensor network by preventing left-out nodes. In: Mandal, J.K., Sinha, D. (eds.) Intelligent Computing Paradigm: Recent Trends. SCI, vol. 784, pp. 43–58. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-7334-3_4

    Chapter  Google Scholar 

  5. Dorigo, M., Di Caro, G.: Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), vol. 2, pp. 1470–1477. IEEE (1999)

    Google Scholar 

  6. Hernández-Ocaña, B., Hernández-Torruco, J., Chávez-Bosquez, O., Calva-Yáñez, M.B., Portilla-Flores, E.A.: Bacterial foraging-based algorithm for optimizing the power generation of an isolated microgrid. Appl. Sci. 9(6), 1261 (2019)

    Article  Google Scholar 

  7. Holland, J.H., et al.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)

    Book  Google Scholar 

  8. Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 760–766. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-30164-8_630

    Chapter  Google Scholar 

  9. Kim, D.H., Abraham, A., Cho, J.H.: A hybrid genetic algorithm and bacterial foraging approach for global optimization. Inf. Sci. 177(18), 3918–3937 (2007)

    Article  Google Scholar 

  10. Lin, N., Tang, J., Li, X., Zhao, L.: A novel improved bat algorithm in UAV path planning. J. Comput. Mater. Contin. 61, 323–344 (2019)

    Google Scholar 

  11. Liu, Y., Yang, Z., Yan, X., Liu, G., Hu, B.: A novel multi-hop algorithm for wireless network with unevenly distributed nodes. Comput. Mater. Contin. 58(1), 79–100 (2019)

    Article  Google Scholar 

  12. Majumder, A., Laha, D., Suganthan, P.N.: Bacterial foraging optimization algorithm in robotic cells with sequence-dependent setup times. Knowl.-Based Syst. 172, 104–122 (2019)

    Article  Google Scholar 

  13. Mantegna, R.N.: Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Phys. Rev. E 49(5), 4677 (1994)

    Article  Google Scholar 

  14. Niu, B., Fan, Y., Wang, H., Li, L., Wang, X.: Novel bacterial foraging optimization with time-varying chemotaxis step. Int. J. Artif. Intell. 7(A11), 257–273 (2011)

    Google Scholar 

  15. Niu, B., Bi, Y., Xie, T.: Structure-redesign-based bacterial foraging optimization for portfolio selection. In: Huang, D.-S., Han, K., Gromiha, M. (eds.) ICIC 2014. LNCS, vol. 8590, pp. 424–430. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09330-7_49

    Chapter  Google Scholar 

  16. Niu, B., Liu, J., Bi, Y., Xie, T., Tan, L.: Improved bacterial foraging optimization algorithm with information communication mechanism. In: 2014 Tenth International Conference on Computational Intelligence and Security, pp. 47–51. IEEE (2014)

    Google Scholar 

  17. Niu, B., Liu, J., Wu, T., Chu, X., Wang, Z., Liu, Y.: Coevolutionary structure-redesigned-based bacterial foraging optimization. IEEE/ACM Trans. Comput. Biol. Bioinform. 15(6), 1865–1876 (2017)

    Article  Google Scholar 

  18. Pang, B., Song, Y., Zhang, C., Wang, H., Yang, R.: Bacterial foraging optimization based on improved chemotaxis process and novel swarming strategy. Appl. Intell. 49(4), 1283–1305 (2018). https://doi.org/10.1007/s10489-018-1317-9

    Article  Google Scholar 

  19. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22(3), 52–67 (2002)

    Article  Google Scholar 

  20. Sahib, M.A., Abdulnabi, A.R., Mohammed, M.A.: Improving bacterial foraging algorithm using non-uniform elimination-dispersal probability distribution. Alexandria Eng. J. 57(4), 3341–3349 (2018)

    Article  Google Scholar 

  21. Tan, L., Lin, F., Wang, H.: Adaptive comprehensive learning bacterial foraging optimization and its application on vehicle routing problem with time windows. Neurocomputing 151, 1208–1215 (2015)

    Article  Google Scholar 

  22. Yan, X., Zhu, Y., Zhang, H., Chen, H., Niu, B.: An adaptive bacterial foraging optimization algorithm with lifecycle and social learning. Discrete Dyn. Nat. Soc. 2012 (2012)

    Google Scholar 

  23. Yang, C., Ji, J., Liu, J., Yin, B.: Bacterial foraging optimization using novel chemotaxis and conjugation strategies. Inf. Sci. 363, 72–95 (2016)

    Article  Google Scholar 

  24. Yang, X.S.: Nature-Inspired Optimization Algorithms. Elsevier, Amsterdam (2014)

    MATH  Google Scholar 

  25. Yang, X.S., Deb, S., Fong, S.: Metaheuristic algorithms: optimal balance of intensification and diversification. Appl. Math. Inf. Sci. 8(3), 977 (2014)

    Article  Google Scholar 

  26. Zhao, W., Wang, L.: An effective bacterial foraging optimizer for global optimization. Inf. Sci. 329, 719–735 (2016)

    Article  Google Scholar 

Download references

Acknowledgment

This work is supported by the National Natural Science Foundation of China (Grant No. 71901152), Natural Science Foundation of Guangdong Province (2018A 030310575), Natural Science Foundation of Shenzhen University (85303/00000155), Project supported by Innovation and Entrepreneurship Research Center of Guangdong University Student (2018A073825), and Research Cultivation Project from Shenzhen Institute of Information Technology (ZY201717).

Author information

Authors and Affiliations

  1. College of Economics, Shenzhen University, Shenzhen, China

    Ming Chen

  2. College of Management, Shenzhen University, Shenzhen, China

    Yikun Ou, Xiaojun Qiu & Hong Wang

Authors
  1. Ming Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yikun Ou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaojun Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong Wang .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science, Nanjing, China

    Jinwei Wang

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chen, M., Ou, Y., Qiu, X., Wang, H. (2020). An Effective Bacterial Foraging Optimization Based on Conjugation and Novel Step-Size Strategies. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12239. Springer, Cham. https://doi.org/10.1007/978-3-030-57884-8_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-57884-8_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57883-1

  • Online ISBN: 978-3-030-57884-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation