Skip to main content
SpringerLink
Log in

Bio-Inspired Optimization Algorithm Based on the Self-defense Mechanism in Plants

  • Conference paper
  • First Online:
Advances in Artificial Intelligence and Soft Computing (MICAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9413))

Included in the following conference series:

Abstract

In this paper the application of a new method of bio-inspired optimization based on the self-defense mechanism of plants is presented. Through time the planet has gone through changes, so plants have had to adapt to these changes and adopt new techniques to defend from natural predators (herbivores). Several works have shown that plants have mechanisms of self-defense to protect themselves from predators. When the plants detect the presence of invading organisms this triggers a series of chemical reactions that are released to air and attract natural predators of the invading organism [1, 9, 10]. For the development of this algorithm we consider as a main idea the predator prey mathematical model of Lotka and Volterra, where two populations are considered and the objective is to maintain a balance between the two populations.

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

Access this chapter

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Institutional subscriptions

References

  1. Bennett, R.N., Wallsgrove, R.M.: Secondary metabolites in plant defense mechanisms. New Phytol. 127(4), 617–633 (1994)

    Article  Google Scholar 

  2. Berryman, A.A.: The origins and evolution of predator-prey theory. Ecology 73, 1530–1535 (1992)

    Google Scholar 

  3. Cruz, J.M.L., González, G.B.: Modelo depredador-presa. Revista de Ciencias Básicas UJAT 7(2), 25–34 (2008)

    Google Scholar 

  4. García-Garrido, J.M., Ocampo, J.A.: Regulation of the plant defense response in arbuscular mycorrhizal symbiosis. J. Exp. Bot. 53(373), 1377–1386 (2002)

    Article  Google Scholar 

  5. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39(3), 459–471 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  6. Law, J.H., Regnier, F.E.: Pheromones. Ann. Rev. Bio-Chem. 40(1), 533–548 (1971)

    Article  Google Scholar 

  7. Lez-Parra, G.G., Arenas, A.J., Cogollo, M.R.: Numerical-analytical solutions of predator-prey models. WSEAS Trans. Biol. Biomed. 10(2), 1–7 (2013)

    Google Scholar 

  8. Melin, P., Olivas, F., Castillo, O., Valdez, F., Soria, J., Valdez, M.: Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic. Expert Syst. Appl. 40(8), 3196–3206 (2013)

    Article  Google Scholar 

  9. Neyoy, H., Castillo, O., Soria, J.: Dynamic fuzzy logic parameter tuning for ACO and its application in TSP problems. In: Castillo, O., Melin, P., Kacprzyk, J. (eds.) Recent Advances on Hybrid Intelligent Systems. SCI, vol. 451, pp. 259–272. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  10. Ordeñana, K.M.: Mecanismos de defensa en las interacciones planta-patógeno. Revista Manejo Integrado de Plagas. Costa Rica 63, 22–32 (2002)

    Google Scholar 

  11. Paré, P.W., Tumlinson, J.H.: Plant volatiles as a defense against insect herbivores. Plant Physiol. 121(2), 325–332 (1999)

    Article  Google Scholar 

  12. Teodorović, D.: Bee colony optimization (BCO). In: Lim, C.P., Jain, L.C., Dehuri, S. (eds.) Innovations in Swarm Intelligence. SCI, vol. 248, pp. 39–60. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  13. Tollsten, L., Müller, P.M.: Volatile organic compounds emitted from beech leaves. Phytochemistry 43, 759–762 (1996)

    Article  Google Scholar 

  14. Vivanco, J.M., Cosio, E., Loyola-Vargas, V.M., Flores, H.E.: Mecanismos químicos de defensa en las plantas. Investigación y ciencia 341(2), 68–75 (2005)

    Google Scholar 

  15. Xiao, Y., Chen, L.: Modeling and analysis of a predator–prey model with disease in the prey. Math. Biosci. 171(1), 59–82 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  16. Yoshida, T., Jones, L.E., Ellner, S.P., Fussmann, G.F., Hairston, N.G.: Rapid evolution drives ecological dynamics in a predator–prey system. Nature 424(6946), 303–306 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Graduate Studies Tijuana Institute of Technology Tijuana, Tijuana, Mexico

    Camilo Caraveo, Fevrier Valdez & Oscar Castillo

Authors
  1. Camilo Caraveo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fevrier Valdez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Oscar Castillo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Camilo Caraveo .

Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City, Mexico

    Grigori Sidorov

  2. Facultad de ciencias, Universidad Autónoma Nacional, México, Distrito Federal, Mexico

    Sofía N. Galicia-Haro

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Caraveo, C., Valdez, F., Castillo, O. (2015). Bio-Inspired Optimization Algorithm Based on the Self-defense Mechanism in Plants. In: Sidorov, G., Galicia-Haro, S. (eds) Advances in Artificial Intelligence and Soft Computing. MICAI 2015. Lecture Notes in Computer Science(), vol 9413. Springer, Cham. https://doi.org/10.1007/978-3-319-27060-9_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27060-9_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27059-3

  • Online ISBN: 978-3-319-27060-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation