Advertisement

Soft Computing

, Volume 22, Issue 15, pp 4907–4920 | Cite as

A new optimization meta-heuristic algorithm based on self-defense mechanism of the plants with three reproduction operators

  • Camilo Caraveo
  • Fevrier Valdez
  • Oscar Castillo
Focus

Abstract

In this paper, a new meta-heuristic algorithm is presented, which is a new bio-inspired optimization algorithm based on the self-defense mechanisms of the plants. In the literature, there are many published works, where the authors scientifically demonstrate that plants have self-defense mechanisms (coping strategies) and these techniques are used to defend themselves from predators, in this case herbivorous insects. The proposed algorithm considers as its basis the predator prey model proposed by Lotka and Volterra, which means that when the plant detects the presence of an invading organism, it triggers a series of chemical reactions, which products are emitted into the air to attract the natural predator of the invading organism. The performance of the proposed approach is verified with the optimization of a set of traditional benchmark mathematical functions and the CEC-2015 functions, and the results are compared statistically against other optimization meta-heuristics.

Keywords

Self-defense of plants Predator–prey models herbivores Levy flights 

Notes

Compliance with ethical standards

Conflict of interest

All the authors in the paper have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Azar D, Fayad K, Daoud C (2016) A combined ant colony optimization and simulated annealing algorithm to assess stability and fault-proneness of classes based on internal software quality attributes. Int J Artif Intell \(^{TM}\) 14(2):137–156Google Scholar
  2. Bennett RN, Wallsgrove RM (1994) Secondary metabolites in plant defense mechanisms. New Phytol 127(4):617–633CrossRefGoogle Scholar
  3. Berryman AA (1992) The origins and evolution of predator-prey theory. Ecology 73(5):1530–1535CrossRefGoogle Scholar
  4. Caraveo C, Valdez F, Castillo O (2015a) A new bio-inspired optimization algorithm based on the self-defense mechanisms of plants. In Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization. Springer, pp 211–218Google Scholar
  5. Caraveo C, Valdez F, Castillo O (2015b) Bio-inspired optimization algorithm based on the self-defense mechanism in plants. In Advances in artificial intelligence and soft computing. Springer, pp 227–237Google Scholar
  6. Cruz JML, González GB (2008) Modelo Depredador-Presa. Revista de Ciencias Básicas UJAT 7(2):25–34Google Scholar
  7. Duan H, Li S, Shi Y (2013) Predator-prey brain storm optimization for DC brushless motor. IEEE Trans Magn 49(10):5336–5340CrossRefGoogle Scholar
  8. Duffy B, Schouten A, Raaijmakers JM (2003) Pathogen self-defense: mechanisms to counteract microbial antagonism. Annu Rev Phytopathol 41(1):501–538CrossRefGoogle Scholar
  9. García-Garrido JM, Ocampo JA (2002) Regulation of the plant defense response in arbuscular mycorrhizal symbiosis. J Exp Bot 53(373):1377–1386CrossRefGoogle Scholar
  10. Heil M, Ton J (2008) Long-distance signalling in plant defence. Trends Plant Sci 13(6):264–272CrossRefGoogle Scholar
  11. Higashitani M, Ishigame A, Yasuda K (2006) Particle swarm optimization considering the concept of predator-prey behavior. In IEEE congress on evolutionary computation, 2006. CEC 2006. IEEE, pp 434–437Google Scholar
  12. Johanyák ZC, Papp O (2012) A hybrid algorithm for parameter tuning in fuzzy model identification. Acta Polytech Hung 9(6):153–165Google Scholar
  13. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471MathSciNetCrossRefzbMATHGoogle Scholar
  14. Kennedy J (2011) Particle swarm optimization. In Encyclopedia of machine learning. Springer, pp 760–766Google Scholar
  15. Kıran MS, Fındık O (2015) A directed artificial bee colony algorithm. Appl Soft Comput 26:454–462CrossRefGoogle Scholar
  16. Laumanns M, Rudolph G, Schwefel HP (1998) A spatial predator-prey approach to multi-objective optimization: a preliminary study. International conference on parallel problem solving from nature. Springer, Berlin, pp 241–249Google Scholar
  17. Law JH, Regnier FE (1971) Pheromones. Ann Rev Bio Chem 40(1):533–548CrossRefGoogle Scholar
  18. Molina D, Herrera F (2015) Iterative hybridization of DE with local search for the CEC’2015 special session on large scale global optimization. In 2015 IEEE congress on evolutionary computation (CEC). IEEE, pp 1974–1978Google Scholar
  19. Neyoy H, Castillo O, Soria J (2013) Dynamic fuzzy logic parameter tuning for ACO and its application in TSP problems. Recent advances on hybrid intelligent systems. Springer, Berlin, pp 259–271CrossRefGoogle Scholar
  20. Ordeñana KM (2002) Mecanismos de defensa en las interacciones planta-patógeno. Revista Manejo Integrado de Plagas Costa Rica 63:22–32Google Scholar
  21. Paré PW, Tumlinson JH (1999) Plant volatiles as a defense against insect herbivores. Plant Physiol 121(2):325–332CrossRefGoogle Scholar
  22. Pieterse CM, Dicke M (2007) Plant interactions with microbes and insects: from molecular mechanisms to ecology. Trends Plant Sci 12(12):564–569CrossRefGoogle Scholar
  23. Precup RE, David RC, Petriu EM, Preitl S, Rădac MB (2014) Novel adaptive charged system search algorithm for optimal tuning of fuzzy controllers. Expert Syst Appl 41(4):1168–1175CrossRefGoogle Scholar
  24. Rhoades DF (1985) Offensive-defensive interactions between herbivores and plants: their relevance in herbivore population dynamics and ecological theory. Am Nat 125(2):205–238CrossRefGoogle Scholar
  25. Ryan CA, Jagendorf A (1995) Self-defense by plants. Proc Nat Acad Sci 92(10):4075CrossRefGoogle Scholar
  26. Teodorovic (2009) Bee colony optimization (BCO). In: Lim CP, Jain LC, Dehuri S, (eds) Innovations in swarm intelligence. Springer, pp 39–60Google Scholar
  27. Tollsten L, Muller PM (1996) Volatile organic compounds emitted from beech leaves. Phytochemistry 43:759–762CrossRefGoogle Scholar
  28. Vivanco JM, Cosio E, Loyola-Vargas VM, Flores HE (2005) Mecanismos químicos de defensa en las plantas. Investigación y ciencia 341(2):68–75Google Scholar
  29. Wang MB, Metzlaff M (2005) RNA silencing and antiviral defense in plants. Curr Opin Plant Biol 8(2):216–222CrossRefGoogle Scholar
  30. Waser NM, Chittka L, Price MV, Williams NM, Ollerton J (1996) Generalization in pollination systems, and why it matters. Ecology 77(4):1043–1060CrossRefGoogle Scholar
  31. Wolfe GV (2000) The chemical defense ecology of marine unicellular plankton: constraints, mechanisms, and impacts. Biol Bull 198(2):225–244CrossRefGoogle Scholar
  32. Xiao Y, Chen L (2001) Modeling and analysis of a predator-prey model with disease in the prey. Math Biosci 171(1):59–82MathSciNetCrossRefzbMATHGoogle Scholar
  33. Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio Inspired Comput 2(2):78–84CrossRefGoogle Scholar
  34. Yang XS (2012) Flower pollination algorithm for global optimization. Unconventional computation and natural computation. Springer, Berlin, pp 240–249CrossRefGoogle Scholar
  35. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In World congress on nature & biologically inspired computing, 2009. NaBIC 2009. IEEE, pp 210–214Google Scholar
  36. Yang XS, Karamanoglu M, He X (2014) Flower pollination algorithm: a novel approach for multi objective optimization. Eng Optim 46(9):1222–1237MathSciNetCrossRefGoogle Scholar
  37. Yoshida T, Jones LE, Ellner SP, Fussmann GF, Hairston NG (2003) Rapid evolution drives ecological dynamics in a predator-prey system. Nature 424(6946):303–306CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Tijuana Institute of TechnologyTijuanaMexico

Personalised recommendations