Comparative Study of Bio-inspired Algorithms Applied in the Optimization of Fuzzy Systems

  • Ivette Miramontes
  • Patricia MelinEmail author
  • German Prado-Arechiga
Part of the Studies in Computational Intelligence book series (SCI, volume 827)


In the medical area, it is very important to have accurate results in diagnosis of diseases that people may suffer. This is why, there is a need to perform the optimization of the fuzzy classifier which provides the nocturnal blood pressure profile of patients, and which is important, due that with this diagnosis we may know if the patient is prone to have a cardiovascular event. This fuzzy system is designed using different membership functions, which are trapezoidal and Gaussian membership functions, in order to select the fuzzy system that provides better results when making the classification. Two bioinspired algorithms are used separately to test their performance, which are the Crow Search Algorithm and Chicken Swarm Optimization. Thirty experiments were performed varying the parameters in the algorithms and from which it can be concluded that the CSO provides better results when optimizing fuzzy systems with both types of membership functions.


Fuzzy systems Optimization Bio-inspired algorithm Nocturnal blood pressure profile 



The authors would like to express thank to the Consejo Nacional de Ciencia y Tecnologia and Tecnologico Nacional de Mexico/Tijuana Institute of Technology for the facilities and resources granted for the development of this research.


  1. 1.
    S. Kumar, G. Kaur, Detection of heart diseases using fuzzy logic. Int. J. Eng. Trends Technol. (IJETT) 4(6), 2694–2699 (2013)Google Scholar
  2. 2.
    X.Y. Djam, Y.H. Kimbi, Fuzzy expert system for the management of hypertension. Pac. J. Sci. Technol. 12(1), 390–402 (2011)Google Scholar
  3. 3.
    Q. Duodu, J.K. Panford, J. Ben Hafron-acquah, Designing algorithm for malaria diagnosis using fuzzy logic for treatment (AMDFLT) in Ghana. Int. J. Comput. Appl. 91(17) (2014)CrossRefGoogle Scholar
  4. 4.
    J.C. Guzman, P. Melin, G. Prado-Arechiga, Design of an optimized fuzzy classifier for the diagnosis of blood pressure with a new computational method for expert rule optimization. Algorithms 10(3), 79 (2017)CrossRefGoogle Scholar
  5. 5.
    X.S. Yang, M. Karamanoglu, X. He, Flower pollination algorithm: a novel approach for multiobjective optimization. Eng. Optim. 46(9), 1222–1237 (2014)MathSciNetCrossRefGoogle Scholar
  6. 6.
    S. Mirjalili, S.M. Mirjalili, A. Lewis, Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  7. 7.
    X.-S. Yang, Firefly Algorithm, Lévy flights and global optimization, in Research and Development in Intelligent Systems XXVI (2010), pp. 209–218Google Scholar
  8. 8.
    M.L. Lagunes, O. Castillo, J. Soria, Methodology for the optimization of a fuzzy controller using a bio-inspired algorithm, in Fuzzy Logic in Intelligent System Design (2018), pp. 131–137Google Scholar
  9. 9.
    J. Perez, P. Melin, O. Castillo, F. Valdez, C. Gonzalez, G. Martinez, Trajectory optimization for an autonomous mobile robot using the bat algorithm, in Fuzzy Logic in Intelligent System Design (2018), pp. 232–241Google Scholar
  10. 10.
    C. Peraza, F. Valdez, P. Melin, Optimization of intelligent controllers using a Type-1 and interval Type-2 fuzzy harmony search algorithm. Algorithms 10(3), 1–17 (2017)MathSciNetzbMATHGoogle Scholar
  11. 11.
    O.R. Carvajal, O. Castillo, J. Soria, Optimization of membership function parameters for fuzzy controllers of an autonomous mobile robot using the flower pollination algorithm. J. Autom. Mob. Robot. Intell. Syst. 12(1), 1–23 (2018)Google Scholar
  12. 12.
    X. Meng, Y. Liu, X. Gao, H. Zhang, A new bio-inspired algorithm: chicken swarm optimization, in Advances in Swarm Intelligence (2014), pp. 86–94Google Scholar
  13. 13.
    A. Askarzadeh, A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169(Supplement C), 1–12 (2016)CrossRefGoogle Scholar
  14. 14.
    J.M. Wilson, Essential cardiology: principles and practice. Tex. Heart Inst. J. 32(4), 616 (2005)Google Scholar
  15. 15.
    B. Wizner, B. Gryglewska, J. Gasowski, J. Kocemba, T. Grodzicki, Normal blood pressure values as perceived by normotensive and hypertensive subjects. J. Hum. Hypertens. 17(2), 87–91 (2003)CrossRefGoogle Scholar
  16. 16.
    O.A. Carretero, S. Oparil, Essential Hypertension. Circulation 101(3), 329–335 (2000)CrossRefGoogle Scholar
  17. 17.
    D. Bloomfield, Night time blood pressure dip. World J. Cardiol. 7(7), 373 (2015)CrossRefGoogle Scholar
  18. 18.
    M. Brian, A. Dalpiaz, E. Matthews, S. Lennon-Edwards, D. Edwards, W. Farquhar, Dietary sodium and nocturnal blood pressure dipping in normotensive men and women. J. Hum. Hypertens. Hypertens. 31, 145–150 (2016)CrossRefGoogle Scholar
  19. 19.
    L.E. Okamoto et al., Nocturnal blood pressure dipping in the hypertension of autonomic failure. Hypertension 53(2), 363–369 (2009)CrossRefGoogle Scholar
  20. 20.
    E. Grossman, Ambulatory blood pressure monitoring in the diagnosis and management of hypertension. Diab. Care 36(Supplement 2), S307–S311 (2013)CrossRefGoogle Scholar
  21. 21.
    O. Friedman, A.G. Logan, Nocturnal blood pressure profiles among normotensive, controlled hypertensive and refractory hypertensive subjects. Can. J. Cardiol. 25(9), e312–e316 (2009)CrossRefGoogle Scholar
  22. 22.
    I. Miramontes, G. Martínez, P. Melin, G. Prado-Arechiga, A hybrid intelligent system model for hypertension diagnosis, in Nature-inspired design of hybrid intelligent systems, ed. by P. Melin, O. Castillo, J. Kacprzyk (Springer International Publishing, Cham, 2017), pp. 541–550Google Scholar
  23. 23.
    I. Miramontes, G. Martínez, P. Melin, G. Prado-Arechiga, A hybrid intelligent system model for hypertension risk diagnosis, in Fuzzy Logic in Intelligent System Design (2018), pp. 202–213Google Scholar
  24. 24.
    P. Melin, I. Miramontes, G. Prado-Arechiga, A hybrid model based on modular neural networks and fuzzy systems for classification of blood pressure and hypertension risk diagnosis. Expert Syst. Appl. 107, 146–164 (2018)CrossRefGoogle Scholar
  25. 25.
    P. Melin, G. Prado-Arechiga, I. Miramontes, J.C. Guzman, Classification of nocturnal blood pressure profile using fuzzy systems. J. Hypertens. 36, e111–e112 (2018)CrossRefGoogle Scholar
  26. 26.
    M.D. Feria-carot, J. Sobrino, Nocturnal hypertension. Hipertens. y riesgo Cardiovasc. 28(4), 143–148 (2011)CrossRefGoogle Scholar
  27. 27.
    P. Melin, A. Mancilla, M. Lopez, O. Mendoza, A hybrid modular neural network architecture with fuzzy Sugeno integration for time series forecasting. Appl. Soft Comput. 7(4), 1217–1226 (2007)CrossRefGoogle Scholar
  28. 28.
    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
  29. 29.
    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
  30. 30.
    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
  31. 31.
    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
  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, D. Sánchez, Multi-objective optimization for modular granular neural networks applied to pattern recognition. Inf. Sci. 460–461, 594–610 (2018)MathSciNetCrossRefGoogle Scholar
  34. 34.
    D. Sánchez, P. Melin, O. Castillo, Optimization of modular granular neural networks using a firefly algorithm for human recognition. Eng. Appl. AI 64, 172–186 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ivette Miramontes
    • 1
  • Patricia Melin
    • 1
    Email author
  • German Prado-Arechiga
    • 1
  1. 1.Tijuana Institute of TechnologyTijuanaMexico

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