Skip to main content
SpringerLink
Log in

A power spectrum optimization algorithm inspired by magnetotactic bacteria

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Magnetotactic bacteria (MTB) are one kind of bacteria with magnetic particles called magnetosomes in their bodies. These particles often connect together like a chain. The MTB move toward the ideal living conditions under the interaction between magnetic field produced by the magnetic particles chain and that of the earth. In the paper, a new magnetic bacteria algorithm based on power spectrum (PSMBA) for optimization is proposed. The candidate solutions are decided by power spectrum in the algorithm. It mainly includes four steps: power spectrum calculation, bacteria swimming, bacteria rotation and bacteria replacement. The effect of swimming schemes and parameter settings on the performance of PSMBA is studied. And it is compared with GA, PSO and its variants and some other optimization algorithms on 25 benchmark functions including CEC2005. The simulation results show that PSMBA has better performance on most of the problems than most of the compared algorithms.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. De Castro LN (2006) Fundamentals of natural computing. Champman & Hall/CRC, Florida, pp 3–20

    MATH  Google Scholar 

  2. Holland JH (1975) Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, MI

  3. Dorigo M, Manianiezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern 26(1):1–13

    Article  Google Scholar 

  4. Eberhart RC Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micromachine and human science, Nagoya, Japan, pp 39–43

  5. Tereshko V (2000) Reaction–diffusion model of a honeybee colony’s foraging behaviour. In: Schoenauer M (ed) Parallel problem solving from nature VI, lecture notes in computer science, vol 1. Springer, Berlin, pp 807–816

    Chapter  Google Scholar 

  6. Bastos-Filho CJA et al. (2008) A novel search algorithm based on fish school behavior. In: Proceedings of the IEEE international conference on systems, man and cyberneticsn (SMC), pp 2646–2651

  7. Krishnanand KN, Ghose D (2009) Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intell 3:87–124

    Article  Google Scholar 

  8. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Article  Google Scholar 

  9. Müeller S, Marchetto J, Airaghi S, Koumoutsakos P (2002) Optimization based on bacterial chemotaxis. IEEE Trans Evol Comput 6:16–29

    Article  Google Scholar 

  10. Pattnaik SS, Bakwad KM, Sohi BS, Ratho RK, Devi S (2013) Swine influenza models based optimization (SIMBO). Appl Soft Comput 13:628–653

    Article  Google Scholar 

  11. Tayarani, MHN, Akbarzadeh T (2008) Magnetic optimization algorithms a new synthesis. In: IEEE Congress on evolutionary computation. Hong Kong, China, pp 2659–2665

  12. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248

    Article  MATH  Google Scholar 

  13. Abdechiri M, Meybodi MR, Bahrami H (2012) Gases Brownian motion optimization: an algorithm for Optimization (GBMO). Appl Soft Comput 1–15:2012

    Google Scholar 

  14. Faivre D, Schuler D (2008) Magnetotactic bacteria and magnetosomes. Chem Rev 108:4875–4898

    Article  Google Scholar 

  15. Mitchell JG, Kogure K (2006) Bacterial motility: links to the environment and a driving force for microbial physics. FEMS Microbiol Ecol 55:3–16

    Article  Google Scholar 

  16. Mo HW (2012) Research on magnetotactic bacteria optimization algorithm. In: The fifth international conference on advanced computational intelligence, pp 423–428

  17. Chemla YR, Grossman HL, Lee TS, Clarke J, Adamkiewicz M, Buchanan BB (1999) A new study of bacterial motion: superconducting quantum interference device microscopy of magnetotactic bacteria. Biophys J 76:3323–3330

    Article  Google Scholar 

  18. Suganthan P, Hansen N, Liang J, Deb K, Chen Y, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Nan yang Technol University, Singapore, pp 1–50

    Google Scholar 

  19. Liu Y, Qin Z, He H (2004) Supervisor-student model in particle swarm optimization. In: IEEE Congress on evolutionary computation (CEC 2004), Edinburgh, UK, p 542–547

  20. Pasupuleti S, Bhattiti R (2006) The gregarious particle swarm optimizer (G-PSO). In: Proceedings of the 8th annual genetic and evolutionary computation conference, Seattle, Washington, USA, pp 67–74

  21. Ratnaweera S, Halgamuge SK, Watson HC (2004) Self organizing hierarchical particle swarm optimization with time varying acceleration coefficients. IEEE Trans Evol Comput 8:240–255

    Article  Google Scholar 

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

    Article  Google Scholar 

  23. Macas M, Lhotska L (2008) Social impact and optimization. Int J Comput Intell Res 4(2):129–136

    Article  Google Scholar 

  24. Senthil M, Arumugam Rao MVC, Tan AWC (2009) A new novel and effective particle swarm optimization like algorithm with extrapolation technique. Int J Appl Soft Comput 9:308–320

    Article  Google Scholar 

  25. Corne D, Dorigo M, Glover F (1999) New ideas in optimization. McGraw-Hill, New York

  26. Karaboga D, Basturk B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132

    Article  MATH  MathSciNet  Google Scholar 

  27. Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295

    Article  Google Scholar 

Download references

Acknowledgments

This work is partially supported by the National Natural Science Foundation of China under Grant No. 61075113, the Excellent Youth Foundation of Heilongjiang Province of China under Grant No. JC201212, the Fundamental Research Funds for the Central Universities No. HEUCFX041306 and Harbin Excellent Discipline Leader, No. 2012RFXXG073.

Author information

Authors and Affiliations

  1. Automation College, Harbin Engineering University, No. 154 Natong Street, Harbin, 150001, China

    Hongwei Mo & Lili Liu

  2. Engineering Training Center, Harbin Engineering University, Harbin, China

    Lifang Xu

Authors
  1. Hongwei Mo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lili Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lifang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongwei Mo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mo, H., Liu, L. & Xu, L. A power spectrum optimization algorithm inspired by magnetotactic bacteria. Neural Comput & Applic 25, 1823–1844 (2014). https://doi.org/10.1007/s00521-014-1672-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-014-1672-3

Keywords

Search

Navigation