Skip to main content
SpringerLink
Log in

A New Metaheuristic Based on the Self-defense Mechanisms of the Plants with a Fuzzy Approach Applied to the CEC2015 Functions

  • Conference paper
  • First Online:
Fuzzy Logic in Intelligent System Design (NAFIPS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 648))

Included in the following conference series:

  • 998 Accesses

Abstract

In this paper a new metaheuristic based on coping strategies of plants with a fuzzy approach is presented. In this work the authors propose a variant of the original algorithm of the plants with a fuzzy approach, The new proposal consists of adding fuzzy logic to adapt the parameters of the algorithm dynamically. In this work, a fuzzy controller is responsible of find the optimal values of the variables α, β, δ, λ, in order to help the algorithm to have a greater performance in solving problems, in the previous works the authors apply the original algorithm to optimization problems, and the parameters of the variables are moved manually, however the results obtained are acceptable in some cases, but we consider that they can be improved using the intelligent technique for the adaptation of parameters.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Amador-Angulo, L., Castillo, O.: Optimization of the Type-1 and Type-2 fuzzy controller design for the water tank using the Bee Colony Optimization. In: 2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW), pp. 1–8. IEEE, June 2014

    Google Scholar 

  2. Amador-Angulo, L., Castillo, O.: A new algorithm based in the smart behavior of the bees for the design of Mamdani-style fuzzy controllers using complex non-linear plants. In: Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization, pp. 617–637. Springer International Publishing (2015)

    Google Scholar 

  3. Amador-Angulo, L., Castillo, O.: Comparative study of bio-inspired algorithms applied in the design of fuzzy controller for the water tank. In: Recent Developments and New Direction in Soft-Computing Foundations and Applications, pp. 419–438. Springer International Publishing (2016)

    Google Scholar 

  4. Amador-Angulo, L., Mendoza, O., Castro, J.R., Rodríguez-Díaz, A., Melin, P., Castillo, O.: Fuzzy sets in dynamic adaptation of parameters of a bee colony optimization for controlling the trajectory of an autonomous mobile robot. Sensors 16(9), 1458 (2016)

    Article  Google Scholar 

  5. Awad, N., Ali, M.Z., Reynolds, R.G.: A differential evolution algorithm with success-based parameter adaptation for CEC2015 learning-based optimization. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 1098–1105. IEEE, May 2015

    Google Scholar 

  6. Caraveo, C., Valdez, F., Castillo, O., Melin, P.: A new metaheuristic based on the self-defense techniques of the plants in nature. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–5. IEEE, December 2016

    Google Scholar 

  7. Céspedes, M., Contreras, M., Cordero, J., Montoya, G., Valverde, K., Rojas, J.D.: A comparison of bio-inspired optimization methodologies applied to the tuning of industrial controllers. In: 2016 IEEE 36th Central American and Panama Convention (CONCAPAN XXXVI), pp. 1–6. IEEE, November 2016

    Google Scholar 

  8. Chen, L., Peng, C., Liu, H.L., Xie, S.: An improved covariance matrix leaning and searching preference algorithm for solving CEC 2015 benchmark problems. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 1041–1045. IEEE, May 2015

    Google Scholar 

  9. Duan, H., Li, P., Yu, Y.: A predator-prey particle swarm optimization approach to multiple UCAV air combat modeled by dynamic game theory. IEEE/CAA J. Autom. Sin. 2(1), 11–18 (2015)

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  11. González, B., Valdez, F., Melin, P., Prado-Arechiga, G.: A gravitational search algorithm for optimization of modular neural networks in pattern recognition. In: Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics, pp. 127–137. Springer International Publishing (2015)

    Google Scholar 

  12. Hancer, E., Karaboga, D.: A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number. Swarm Evol. Comput. 32, 49–67 (2017)

    Article  Google Scholar 

  13. Laumanns, M., Rudolph, G., Schwefel, H.P.: A spatial predator-prey approach to multi-objective optimization: a preliminary study. In: Parallel Problem Solving from Nature—PPSN V, pp. 241–249. Springer, Heidelberg (1998)

    Google Scholar 

  14. Lawson, L.M., Spitz, Y.H., Hofmann, E.E., Long, R.B.: A data assimilation technique applied to a predator-prey model. Bull. Math. Biol. 57(4), 593–617 (1995)

    Article  MATH  Google Scholar 

  15. Li, M., Yang, S., Liu, X.: Pareto or non-pareto: bi-criterion evolution in multiobjective optimization. IEEE Trans. Evol. Comput. 20(5), 645–665 (2016)

    Article  Google Scholar 

  16. Li, X.: A real-coded predator-prey genetic algorithm for multiobjective optimization. In: International Conference on Evolutionary Multi-Criterion Optimization, pp. 207–221. Springer, Heidelberg, April 2003

    Google Scholar 

  17. Méndez, E., Castillo, O., Soria, J., Sadollah, A.: Fuzzy dynamic adaptation of parameters in the water cycle algorithm. In: Nature-Inspired Design of Hybrid Intelligent Systems, pp. 297–311. Springer International Publishing (2017)

    Google Scholar 

  18. Peraza, C., Valdez, F., Castillo, O.: An improved harmony search algorithm using fuzzy logic for the optimization of mathematical functions. In: Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization, pp. 605–615. Springer International Publishing (2015)

    Google Scholar 

  19. Pérez, J., Valdez, F., Castillo, O.: Modification of the bat algorithm using fuzzy logic for dynamical parameter adaptation. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 464–471. IEEE, May 2015

    Google Scholar 

  20. Tanweer, M.R., Suresh, S., Sundararajan, N.: Improved SRPSO algorithm for solving CEC 2015 computationally expensive numerical optimization problems. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 1943–1949. IEEE, May 2015

    Google Scholar 

  21. Valdez, F., Vazquez, J.C., Melin, P., Castillo, O.: Comparative study of the use of fuzzy logic in improving particle swarm optimization variants for mathematical functions using co-evolution. Appl. Soft Comput. 52, 1070–1083 (2017)

    Article  Google Scholar 

  22. Zeng, Z.: Optimization problem of a predator-prey system with Holling II functional response. In: 2010 29th Chinese Control Conference (CCC), pp. 5483–5485. IEEE, July 2010

    Google Scholar 

  23. Zhang, B., Duan, H.: Three-dimensional path planning for uninhabited combat aerial vehicle based on predator-prey pigeon-inspired optimization in dynamic environment. IEEE/ACM Trans. Comput. Biol. Bioinform. (TCBB) 14(1), 97–107 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Graduate Studies, Tijuana Institute of Technology, 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 Fevrier Valdez .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  4. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Marek Reformat

  5. Laboratory of Computational Intelligence and Automation, University of Waterloo, Waterloo, Ontario, Canada

    William Melek

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Caraveo, C., Valdez, F., Castillo, O. (2018). A New Metaheuristic Based on the Self-defense Mechanisms of the Plants with a Fuzzy Approach Applied to the CEC2015 Functions. In: Melin, P., Castillo, O., Kacprzyk, J., Reformat, M., Melek, W. (eds) Fuzzy Logic in Intelligent System Design. NAFIPS 2017. Advances in Intelligent Systems and Computing, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-319-67137-6_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67137-6_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67136-9

  • Online ISBN: 978-3-319-67137-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation