Chemical Reaction Algorithm to Control Problems

  • David de la O
  • Oscar CastilloEmail author
  • José Soria
Part of the Studies in Computational Intelligence book series (SCI, volume 862)


In this paper, we developed an adaptation of the reactions of Chemical Reaction Algorithm (CRA), originally proposed by Astudillo et al. in 2011, which uses fixed parameters in its 4 reactions. We propose a modification to the functions within the chemical reactions, which will help in the optimization of control problems. Using the robot plant “Probot” proposed method show good results in the robot plant.


Chemical reaction algorith Fuzzy Adaptation Parameters 


  1. 1.
    Astudillo, L., Melin, P., Castillo, O.: Nature optimization applied to design a type-2 fuzzy controller for an autonomous mobile robot. In: 2012 Fourth World Congress on de Nature and Biologically Inspired Computing (NaBIC) (2012)Google Scholar
  2. 2.
    Melin, P., Astudillo, L., Castillo, O., Valdez, F., Garcia, M.: Optimal design of type-2 and type-1 fuzzy tracking controllers for autonomous mobile robots under perturbed torques using a new chemical optimization paradigm. Expert Syst. Appl. 40(8), 3185–3195 (2013)CrossRefGoogle Scholar
  3. 3.
    Astudillo, L., Melin, P., Castillo, O.: Introduction to an optimization algorithm based on the chemical reactions. Inf. Sci. 291, 85–95 (2015)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Sanchez, C., Melin, P., Astudillo, L.: Chemical optimization method for modular neural networks applied in emotion classification. In: Castillo, O., Melin, P., Kacprzyk, J. (eds.) Recent Advances on Hybrid Approaches for Designing Intelligent Systems, vol. 547, pp. 381–390. Springer, Berlin (2014)CrossRefGoogle Scholar
  5. 5.
    de la O.D., Castillo, O., Melendez, A., Astudillo, L.: Optimization of a reactive controller for mobile robots based on CRA. In: Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC), IEEE, pp. 1–6, Aug 2015Google Scholar
  6. 6.
    de la, O.D., Castillo, O., Astudillo, L., Soria, J.: Fuzzy chemical reaction algorithm with dynamic adaptation of parameters. In: Melin, P., Castillo, O., Kacprzyk, J., Reformat, M., Melek, W. (eds.) Fuzzy Logic in Intelligent System Design, pp. 122–130. Springer, Cham (2018)Google Scholar
  7. 7.
    Olivas, F., Valdez, F., Castillo, O.: An interval type-2 fuzzy logic system for dynamic parameter adaptation in particle swarm optimization. In: 2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW), vol. 1, no. 6, pp. 24–26, June 2014Google Scholar
  8. 8.
    Amezcua, J., Melin, P.: Optimization of modular neural networks with the LVQ algorithm for classification of arrhythmias using particle swarm optimization. In: Recent Advances on Hybrid Approaches for Designing Intelligent Systems, pp. 307–314 (2014)Google Scholar
  9. 9.
    Valdez, F., Melin, P., Castillo, O.: A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation. Expert Syst. Appl. 41(14), 6459–6466 (2014)CrossRefGoogle Scholar
  10. 10.
    Cervantes, L., Castillo, O.: Design of a fuzzy system for the longitudinal control of an F-14 airplane. In: Soft Computing for Intelligent Control and Mobile Robotics, pp. 213–224 (2011)Google Scholar
  11. 11.
    de la, O.D., Castillo, O., Soria, J.: Optimization of reactive control for mobile robots based on the CRA using type-2 fuzzy logic. Stud. Comput. Intell. 667(1), 505–518 (2017)Google Scholar
  12. 12.
    Wang, D., Wang, G., Hu, R.: Parameters optimization of fuzzy controller based on PSO. In: 3rd International Conference on Intelligent System and Knowledge Engineering, ISKE 2008, vol. 1, pp. 599, 603, 17–19 Nov 2008Google Scholar
  13. 13.
    Esmin, A.A.A, Aoki, A.R., Lambert-Torres, G.: Particle swarm optimization for fuzzy membership functions optimization. In: 2002 IEEE International Conference on Systems, Man and Cybernetics, vol. 3, p. 6. 6–9 Oct 2002Google Scholar
  14. 14.
    Fierro, R., Castillo, O.: Design of fuzzy control systems with different PSO variants. Recent Advances on Hybrid Intelligent Systems, pp. 81–88 (2013)CrossRefGoogle Scholar
  15. 15.
    Fang, G., Kwok, N.M., Quang, H.: Automatic fuzzy membership function tuning using the particle swarm optimization. In: Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA ‘08, vol. 2, pp. 324, 328, 19–20 Dec 2008Google Scholar
  16. 16.
    Li, H.X., Gatland, H.B.: A new methodology for designing a fuzzy logic controller. IEEE Trans. Syst. Man Cybern. 25(3), 505–512 (1995)CrossRefGoogle Scholar
  17. 17.
    Martínez, R., Castillo, O., Soria, J.: Particle swarm optimization applied to the design of type-1 and type-2 fuzzy controllers for an autonomous Mobile Robot. In: Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition, pp. 247–262 (2009)Google Scholar
  18. 18.
    Melendez, A., Castillo, O.: Optimization of type-2 fuzzy reactive controllers for an autonomous mobile robot. In: Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 207–211 (2012)Google Scholar
  19. 19.
    Melendez, A., Castillo, O.: Evolutionary optimization of the fuzzy integra-tor in a navigation system for a mobile robot. In: Castillo, O., Melin, P., Kacprzyk, J. (eds.) Recent Advances on Hybrid Intelligent Systems, volume 451 of Studies in Computational Intelligence, pp. 21–31. Springer, Berlin, Heidelberg (2013)CrossRefGoogle Scholar
  20. 20.
    Melin, P., Castillo, O.: Intelligent control of complex electrochemical systems with a neuro-fuzzy-genetic approach. IEEE Trans. Industr. Electron. 48(5), 951–955 (2001)CrossRefGoogle Scholar
  21. 21.
    Gonzalez, C.I., Melin, P., Castro, J.R., Castillo, O., Mendoza, O.: Optimization of interval type-2 fuzzy systems for image edge detection. Appl. Soft Comput. 47, 631–643 (2016)CrossRefGoogle Scholar
  22. 22.
    Olivas, F., Valdez, F., Castillo, O., Gonzalez, C.I., Martinez, G., Melin, P.: Ant colony optimization with dynamic parameter adaptation based on interval type-2 fuzzy logic systems. Appl. Soft Comput. 53, 74–87 (2017)CrossRefGoogle Scholar
  23. 23.
    Sanchez, M.A., Castillo, O., Castro, J.R., Melin, P.: Fuzzy granular gravitational clustering algorithm for multivariate data. Inf. Sci. 279, 498–511 (2014)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Sánchez, D., Melin, P.: Optimization of modular granular neural networks using hierarchical genetic algorithms for human recognition using the ear biometric measure. Eng. Appl. Artif. Intell. 27, 41–56 (2014)CrossRefGoogle Scholar
  25. 25.
    Sanchez, M.A., Castro, J.R., Castillo, O., Mendoza, O., Rodriguez-Diaz, A., Melin, P.: Fuzzy higher type information granules from an uncertainty measurement. Granul. Comput. 2(2), 95–103 (2017)CrossRefGoogle Scholar
  26. 26.
    Gaxiola, F., Melin, P., Valdez, F., Castro, J.R., Castillo, O.: Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO. Appl. Soft Comput. 38, 860–871 (2016)CrossRefGoogle Scholar
  27. 27.
    Castillo, O., Castro, J.R., Melin, P., Rodriguez-Diaz, A.: Application of interval type-2 fuzzy neural networks in non-linear identification and time series prediction. Soft. Comput. 18(6), 1213–1224 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Tijuana Institute of TechnologyTijuanaMexico

Personalised recommendations