Magnetotactic Bacteria Optimization Algorithm Based on Moment Interaction Energy

  • Lifang Xu
  • Hongwei MoEmail author
  • Jiao Zhao
  • Chaomin Luo
  • Zhenzhong Chu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10385)


In this paper, an improved magnetotactic bacteria optimization algorithm (IMBOA) is proposed to solve unconstrained optimization problems. IMBOA uses an archive to keep some better solutions in order to guide the moving of the whole population in each generation. And it uses a kind of efficient interaction energy to enhance diversity of the population for encouraging broader exploration. The proposed algorithm is compared with some relative optimization algorithms on the CEC 2013 real-parameter optimization benchmark functions. Experimental results show that the proposed algorithm IMBOA has better performance than the compared algorithms on most of the benchmark problems.


Magnetotactic bacteria optimization algorithm Efficient interaction energy Diversity Archive 



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.


  1. 1.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)Google Scholar
  2. 2.
    Tereshko, V.: Reaction-diffusion model of a honeybee colony’s foraging behaviour. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.-P. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 807–816. Springer, Heidelberg (2000). doi: 10.1007/3-540-45356-3_79 CrossRefGoogle Scholar
  3. 3.
    Müeller, S., Marchetto, J., Airaghi, S., Koumoutsakos, P.: Optimization based on bacterial chemotaxis. IEEE Trans. Evol. Comput. 6, 16–29 (2002)CrossRefGoogle Scholar
  4. 4.
    Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-04944-6_14 CrossRefGoogle Scholar
  5. 5.
    Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: Proceedings of World Congress on Nature & Biologically Inspired Computing (NaBic 2009), USA, pp. 210–214. IEEE Publications (2009)Google Scholar
  6. 6.
    Mo, H.W.: Research on magnetotactic bacteria optimization algorithm. In: The Fifth International Conference on Advanced Computational Intelligence, pp. 423–428 (2012)Google Scholar
  7. 7.
    Faivre, D., Schuler, D.: Magnetotactic bacteria and magnetosomes. Chem. Rev. 108, 4875–4898 (2008)CrossRefGoogle Scholar
  8. 8.
    Mo, H.W., Liu, L.L., Xu, L.F., Zhao, Y.Y.: Research on magnetotactic bacteria optimization algorithm based on the best individual. In: The Sixth International Conference on Bio-inspired Computing, Wuhan, China, pp. 318–322 (2014)Google Scholar
  9. 9.
    Mo, H.W., Liu, L.L., Xu, L.F.: A power spectrum optimization algorithm inspired by magnetotactic bacteria. Neural Comput. Appl. 25(7), 1823–1844 (2014)CrossRefGoogle Scholar
  10. 10.
    Mo, H.W., Liu, L.L., Zhao, J.: A new magnetotactic bacteria optimization algorithm based on moment migration. IEEE/ACM Trans. Comput. Biol. Bioinform. 14(1), 15–26 (2017)CrossRefGoogle Scholar
  11. 11.
    Liang, J., Qu, B.Y., Suganthan, P., Hernández-Díaz, A.: Problem definitions and evaluation criteria for the CEC 2013 special session and competition on real-parameter optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical report, Nanyang Technological University, Singapore, Technical report (2013)Google Scholar
  12. 12.
    Wang, Y., Cai, Z., Zhang, Q.: Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans. Evol. Comput. 15(1), 55–66 (2011)CrossRefGoogle Scholar
  13. 13.
    Garcia-Martinez, C., Lozano, M., Herrera, F., Molina, D., Sanchez, A.M.: Global and local real-coded genetic algorithms based on parent-centric crossover operators. Eur. J. Oper. Res. 185(3), 1088–1113 (2008)CrossRefzbMATHGoogle Scholar
  14. 14.
    Chen, W.N., et al.: Particle swarm optimization with an aging leader and challengers. IEEE Trans. Evol. Comput. 17(2), 241–258 (2013)CrossRefGoogle Scholar
  15. 15.
    Mallipeddi, R., Suganthan, P.N., Pan, Q.K., Tasgetiren, M.F.: Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl. Soft Comput. 11(2), 1679–1696 (2011)CrossRefGoogle Scholar
  16. 16.
    Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8(3), 204–210 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lifang Xu
    • 1
  • Hongwei Mo
    • 2
    Email author
  • Jiao Zhao
    • 2
  • Chaomin Luo
    • 3
  • Zhenzhong Chu
    • 4
  1. 1.Engineering Training CenterHarbin Engineering UniversityHarbinChina
  2. 2.Automation CollegeHarbin Engineering UniversityHarbinChina
  3. 3.Department of Electrical and Computer EngineeringUniversity of Detroit MercyDetroitUSA
  4. 4.College of Information EngineeringShanghai Maritime UniversityPudongChina

Personalised recommendations