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Constrained Real-Parameter Optimization Using the Firefly Algorithm and the Grey Wolf Optimizer

  • Luis Rodríguez
  • Oscar CastilloEmail author
  • Mario García
  • José Soria
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 827)

Abstract

The main goal of this paper is to present the performance of two popular algorithms, the first is the Firefly Algorithm (FA) and the second one is the Grey Wolf Optimizer (GWO) algorithm for complex problems. In this case the problems that we are presenting are of the CEC 2017 Competition on Constrained Real-Parameter Optimization in order to realize a brief analysis, study and comparison between the FA and GWO algorithms respectively.

Keywords

Grey wolf optimizer Firefly algorithm Constraints Complex problems Study Optimization 

References

  1. 1.
    H.R. Maier, Z. Kapelan, Evolutionary algorithms and other metaheuritics in water resources: Current status, research challenges and future directions. Environ. Model Softw. 62, 271–299 (2014)CrossRefGoogle Scholar
  2. 2.
    U. Can, Alatas B: physics based metaheuristic algorithms for global optimization. Am. J. Inf. Sci. Comput. Eng. 1, 94–106 (2015)Google Scholar
  3. 3.
    X. Yang, M. Karamanoglu, Swarm intelligence and bio-inspired computation: an overview. Swarm Intell. Bio-Inspired Comput., 3–23 (2013)Google Scholar
  4. 4.
    D.H. Wolpert, W.G. Macready, No free lunch theorems for optimization. Evolut. Comput. IEEE Trans. 1, 67–82 (1997)CrossRefGoogle Scholar
  5. 5.
    X.-S. Yang, Firefly Algorithm, Lévy Flights and Global Optimization arXiv:1003.1464v1 (2010)
  6. 6.
    X.-S. Yang, Firefly Algorithm: Recent Advances and Applications arXiv:1308.3898v1 (2013)
  7. 7.
    S. Mirjalili, M. Mirjalili, A. Lewis, Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  8. 8.
    C. Muro, R. Escobedo, L. Spector, R. Coppinger, Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations. Behav. Process. 88, 192–197 (2011)CrossRefGoogle Scholar
  9. 9.
    L. Rodríguez, O. Castillo, M. Valdez, J. Soria, A comparative study of dynamic adaptation of parameters in the GWO algorithm using type-1 and interval type-2 fuzzy logic. Fuzzy Logic Augmentation Neural Optim. Algorithms: Theor. Aspects Real Appl., 3–17 (2018)Google Scholar
  10. 10.
    J. Digalakis, K. Margaritis, On benchmarking functions for genetic algorithms. Int. J. Comput. Math. 77, 481–506 (2001)MathSciNetCrossRefGoogle Scholar
  11. 11.
    M. Molga, C. Smutnicki, Test functions for optimization needs. Test functions for optimization needs (2005)Google Scholar
  12. 12.
    X.-S. Yang, Test problems in optimization. arXiv, preprint arXiv: 1008.0549 (2010)Google Scholar
  13. 13.
    W. Guohua, R. Mallipeddi, P.N. Suganthan, Problem Definitions and Evaluation Criteria for the CEC 2017 Competition on Constrained Real-Parameter Optimization (2017)Google Scholar
  14. 14.
    M. Lagunes, O. Castillo J. Soria, Optimization of membership functions parameters for fuzzy controller of an autonomous mobile robot using the firefly algorithm, in Fuzzy Logic Augmentation of Neural and Optimization Algorithms (2018), pp 199–206Google Scholar
  15. 15.
    L. Rodriguez, O. Castillo, J. Soria, P. Melin, F. Valdez, C. Gonzalez, G. Martinez, J. Soto, A fuzzy hierarchical operator in the grey wolf optimizer algorithm. Appl. Soft Comput. 57, 315–328 (2017)CrossRefGoogle Scholar
  16. 16.
    R. Larson, B. Farber, Elementary Statistics Picturing the World (Pearson Education Inc. 2003), pp. 428–433Google Scholar
  17. 17.
    B. Gonzalez, P. Melin, F. Valdez, G. Prado-Arechiga, Ensemble neural network optimization using a gravitational search algorithm with interval type-1 and type-2 fuzzy parameter adaptation in pattern recognition applications, in Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications (2018), pp 17–27Google Scholar
  18. 18.
    E. Bernal, O. Castillo, J. Soria, Imperialist competitive algorithm with dynamic parameter adaptation applied to the optimization of mathematical functions. Nat.-Inspired Des. Hybrid Int. Syst. (2017), pp 329–341Google Scholar
  19. 19.
    J. Barraza, P. Melin, F. Valdez, C.I. Gonzalez, Fuzzy Fireworks Algorithm Based on a Sparks Dispersion Measure, Algorithms, vol. 10 (2017)CrossRefGoogle Scholar
  20. 20.
    J. Barraza, P. Melin, F. Valdez, C. Gonzalez, Fuzzy FWA with dynamic adaptation of parameters, in IEEE CEC (2016), pp. 4053–4060Google Scholar
  21. 21.
    L. Rodríguez, O. Castillo, M. García, J. Soria, A comparative study of dynamic adaptation of parameters in the GWO algorithm using type-1 and interval type-2 fuzzy logic, in Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications (2018), pp 3–16Google Scholar
  22. 22.
    C. Caraveo, A. Fevrier O. Castillo, Optimization mathematical functions for multiple variables using the algorithm of self-defense of the plants. Nat.-Inspired Des. Hybrid Intell. Syst., 631–640 (2017)Google Scholar
  23. 23.
    M. Guerrero, O. Castillo, M. Garcia, Cuckoo search algorithm via Lévy flight with dynamic adaptation of parameter using fuzzy logic for benchmark mathematical functions, in Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. Studies in Computational Intelligence (2016), pp 555–571CrossRefGoogle Scholar
  24. 24.
    C. Leal Ramírez, O. Castillo, P. Melin, A. Rodríguez Díaz, Simulation of the bird age-structured population growth based on an interval type-2 fuzzy cellular structure. Inf. Sci. 181(3), 519–535 (2011)MathSciNetCrossRefGoogle Scholar
  25. 25.
    N.R. Cázarez-Castro, L.T. Aguilar, O. Castillo, Designing type-1 and type-2 fuzzy logic controllers via fuzzy Lyapunov synthesis for nonsmooth mechanical systems. Eng. Appl. AI 25(5), 971–979 (2012)CrossRefGoogle Scholar
  26. 26.
    E. Rubio, O. Castillo, F. Valdez, P. Melin, C.I. González, G. Martinez: An extension of the fuzzy possibilistic clustering algorithm using type-2 fuzzy logic techniques. Adv. Fuzzy Syst., 7094046:1-7094046:23 (2017)CrossRefGoogle Scholar
  27. 27.
    O. Castillo, P. Melin, Intelligent systems with interval type-2 fuzzy logic. Int. J. Innov. Comput. Inf. Control 4(4), 771–783 (2008)Google Scholar
  28. 28.
    G.M. Mendez, O. Castillo, Interval type-2 TSK fuzzy logic systems using hybrid learning algorithm, fuzzy systems, in The 14th IEEE International Conference on FUZZ’05 (2005), pp. 230–235Google Scholar
  29. 29.
    P. Melin, C.I. González, J.R. Castro, O. Mendoza, O. Castillo, Edge-detection method for image processing based on generalized Type-2 fuzzy logic. IEEE Trans. Fuzzy Syst. 22(6), 1515–1525 (2014)CrossRefGoogle Scholar
  30. 30.
    C.I. González, P. Melin, J.R. Castro, O. Castillo, O. Mendoza, Optimization of interval type-2 fuzzy systems for image edge detection. Appl. Soft Comput. 47, 631–643 (2016)CrossRefGoogle Scholar
  31. 31.
    C.I. González, P. Melin, J.R. Castro, O. Mendoza, O. Castillo, An improved Sobel edge detection method based on generalized type-2 fuzzy logic. Soft. Comput. 20(2), 773–784 (2016)CrossRefGoogle Scholar
  32. 32.
    E. Ontiveros, P. Melin, O. Castillo, High order α-planes integration: a new approach to computational cost reduction of general type-2 fuzzy systems. Eng. Appl. AI 74, 186–197 (2018)CrossRefGoogle Scholar
  33. 33.
    P. Melin, O. Castillo, Intelligent control of complex electrochemical systems with a neuro-fuzzy-genetic approach. IEEE Trans. Ind. Electron. 48(5), 951–955Google Scholar
  34. 34.
    L. Aguilar, P. Melin, O. Castillo, Intelligent control of a stepping motor drive using a hybrid neuro-fuzzy ANFIS approach. Appl. Soft Comput. 3(3), 209–219 (2003)Google Scholar
  35. 35.
    P. Melin, O. Castillo, Adaptive intelligent control of aircraft systems with a hybrid approach combining neural networks, fuzzy logic and fractal theory. Appl. Soft Comput. 3(4), 353–362 (2003)Google Scholar
  36. 36.
    P. Melin, J. Amezcua, F. Valdez, O. Castillo, A new neural network model based on the LVQ algorithm for multi-class classification of arrhythmias. Inf. Sci. 279, 483–497 (2014)MathSciNetCrossRefGoogle Scholar
  37. 37.
    P. Melin, O. Castillo, Modelling, Simulation and Control of Non-Linear Dynamical Systems: An Intelligent Approach Using Soft Computing and Fractal Theory (CRC Press, 2001)Google Scholar
  38. 38.
    P. Melin, D. Sánchez, O. Castillo, Genetic optimization of modular neural networks with fuzzy response integration for human recognition. Inf. Sci. 197, 1–19 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Luis Rodríguez
    • 1
  • Oscar Castillo
    • 1
    Email author
  • Mario García
    • 1
  • José Soria
    • 1
  1. 1.Tijuana Institute of TechnologyTijuanaMexico

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