Skip to main content

2017 | OriginalPaper | Buchkapitel

Magnetotactic Bacteria Optimization Algorithm Based on Moment Interaction Energy

verfasst von : Lifang Xu, Hongwei Mo, Jiao Zhao, Chaomin Luo, Zhenzhong Chu

Erschienen in: Advances in Swarm Intelligence

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995) Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)
2.
Zurück zum Zitat 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 CrossRef 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 CrossRef
3.
Zurück zum Zitat Müeller, S., Marchetto, J., Airaghi, S., Koumoutsakos, P.: Optimization based on bacterial chemotaxis. IEEE Trans. Evol. Comput. 6, 16–29 (2002)CrossRef Müeller, S., Marchetto, J., Airaghi, S., Koumoutsakos, P.: Optimization based on bacterial chemotaxis. IEEE Trans. Evol. Comput. 6, 16–29 (2002)CrossRef
5.
Zurück zum Zitat 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) 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)
6.
Zurück zum Zitat Mo, H.W.: Research on magnetotactic bacteria optimization algorithm. In: The Fifth International Conference on Advanced Computational Intelligence, pp. 423–428 (2012) Mo, H.W.: Research on magnetotactic bacteria optimization algorithm. In: The Fifth International Conference on Advanced Computational Intelligence, pp. 423–428 (2012)
7.
Zurück zum Zitat Faivre, D., Schuler, D.: Magnetotactic bacteria and magnetosomes. Chem. Rev. 108, 4875–4898 (2008)CrossRef Faivre, D., Schuler, D.: Magnetotactic bacteria and magnetosomes. Chem. Rev. 108, 4875–4898 (2008)CrossRef
8.
Zurück zum Zitat 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) 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)
9.
Zurück zum Zitat 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)CrossRef 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)CrossRef
10.
Zurück zum Zitat 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)CrossRef 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)CrossRef
11.
Zurück zum Zitat 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) 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)
12.
Zurück zum Zitat 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)CrossRef 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)CrossRef
13.
Zurück zum Zitat 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)CrossRefMATH 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)CrossRefMATH
14.
Zurück zum Zitat Chen, W.N., et al.: Particle swarm optimization with an aging leader and challengers. IEEE Trans. Evol. Comput. 17(2), 241–258 (2013)CrossRef Chen, W.N., et al.: Particle swarm optimization with an aging leader and challengers. IEEE Trans. Evol. Comput. 17(2), 241–258 (2013)CrossRef
15.
Zurück zum Zitat 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)CrossRef 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)CrossRef
16.
Zurück zum Zitat Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8(3), 204–210 (2004)CrossRef Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8(3), 204–210 (2004)CrossRef
Metadaten
Titel
Magnetotactic Bacteria Optimization Algorithm Based on Moment Interaction Energy
verfasst von
Lifang Xu
Hongwei Mo
Jiao Zhao
Chaomin Luo
Zhenzhong Chu
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-61824-1_9

Premium Partner