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

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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.

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References

  • 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):137–156

  • Bennett RN, Wallsgrove RM (1994) Secondary metabolites in plant defense mechanisms. New Phytol 127(4):617–633

    Article  Google Scholar 

  • Berryman AA (1992) The origins and evolution of predator-prey theory. Ecology 73(5):1530–1535

    Article  Google Scholar 

  • 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 211–218

  • 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 227–237

  • Cruz JML, González GB (2008) Modelo Depredador-Presa. Revista de Ciencias Básicas UJAT 7(2):25–34

    Google Scholar 

  • Duan H, Li S, Shi Y (2013) Predator-prey brain storm optimization for DC brushless motor. IEEE Trans Magn 49(10):5336–5340

    Article  Google Scholar 

  • Duffy B, Schouten A, Raaijmakers JM (2003) Pathogen self-defense: mechanisms to counteract microbial antagonism. Annu Rev Phytopathol 41(1):501–538

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Heil M, Ton J (2008) Long-distance signalling in plant defence. Trends Plant Sci 13(6):264–272

    Article  Google Scholar 

  • 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 434–437

  • Johanyák ZC, Papp O (2012) A hybrid algorithm for parameter tuning in fuzzy model identification. Acta Polytech Hung 9(6):153–165

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  • Kennedy J (2011) Particle swarm optimization. In Encyclopedia of machine learning. Springer, pp 760–766

  • Kıran MS, Fındık O (2015) A directed artificial bee colony algorithm. Appl Soft Comput 26:454–462

    Article  Google Scholar 

  • 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 241–249

    Google Scholar 

  • Law JH, Regnier FE (1971) Pheromones. Ann Rev Bio Chem 40(1):533–548

    Article  Google Scholar 

  • 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 1974–1978

  • 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 259–271

    Chapter  Google Scholar 

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

    Google Scholar 

  • Paré PW, Tumlinson JH (1999) Plant volatiles as a defense against insect herbivores. Plant Physiol 121(2):325–332

    Article  Google Scholar 

  • Pieterse CM, Dicke M (2007) Plant interactions with microbes and insects: from molecular mechanisms to ecology. Trends Plant Sci 12(12):564–569

    Article  Google Scholar 

  • 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):1168–1175

    Article  Google Scholar 

  • Rhoades DF (1985) Offensive-defensive interactions between herbivores and plants: their relevance in herbivore population dynamics and ecological theory. Am Nat 125(2):205–238

    Article  Google Scholar 

  • Ryan CA, Jagendorf A (1995) Self-defense by plants. Proc Nat Acad Sci 92(10):4075

    Article  Google Scholar 

  • Teodorovic (2009) Bee colony optimization (BCO). In: Lim CP, Jain LC, Dehuri S, (eds) Innovations in swarm intelligence. Springer, pp 39–60

  • Tollsten L, Muller PM (1996) Volatile organic compounds emitted from beech leaves. Phytochemistry 43:759–762

    Article  Google Scholar 

  • Vivanco JM, Cosio E, Loyola-Vargas VM, Flores HE (2005) Mecanismos químicos de defensa en las plantas. Investigación y ciencia 341(2):68–75

    Google Scholar 

  • Wang MB, Metzlaff M (2005) RNA silencing and antiviral defense in plants. Curr Opin Plant Biol 8(2):216–222

    Article  Google Scholar 

  • Waser NM, Chittka L, Price MV, Williams NM, Ollerton J (1996) Generalization in pollination systems, and why it matters. Ecology 77(4):1043–1060

    Article  Google Scholar 

  • Wolfe GV (2000) The chemical defense ecology of marine unicellular plankton: constraints, mechanisms, and impacts. Biol Bull 198(2):225–244

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  • Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio Inspired Comput 2(2):78–84

    Article  Google Scholar 

  • Yang XS (2012) Flower pollination algorithm for global optimization. Unconventional computation and natural computation. Springer, Berlin, pp 240–249

    Chapter  Google Scholar 

  • Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In World congress on nature & biologically inspired computing, 2009. NaBIC 2009. IEEE, pp 210–214

  • Yang XS, Karamanoglu M, He X (2014) Flower pollination algorithm: a novel approach for multi objective optimization. Eng Optim 46(9):1222–1237

    Article  MathSciNet  Google Scholar 

  • Yoshida T, Jones LE, Ellner SP, Fussmann GF, Hairston NG (2003) Rapid evolution drives ecological dynamics in a predator-prey system. Nature 424(6946):303–306

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Camilo Caraveo, Fevrier Valdez & Oscar Castillo

Authors
  1. Camilo Caraveo
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  2. Fevrier Valdez
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  3. Oscar Castillo
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Correspondence to Oscar Castillo.

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

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

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Communicated by C. Kahraman.

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Caraveo, C., Valdez, F. & Castillo, O. A new optimization meta-heuristic algorithm based on self-defense mechanism of the plants with three reproduction operators. Soft Comput 22, 4907–4920 (2018). https://doi.org/10.1007/s00500-018-3188-8

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