The Cuckoo Search Algorithm Applied to Fuzzy Logic Control Parameter Optimization

  • G. García-Gutiérrez
  • D. Arcos-AvilesEmail author
  • E. V. Carrera
  • F. Guinjoan
  • A. Ibarra
  • P. Ayala
Part of the Springer Tracts in Nature-Inspired Computing book series (STNIC)


In the design of control systems, the tuning of controller parameters has a fundamental role in the performance of both transient and steady-state regimes. From this perspective, the tuning of controller parameters has been carried out using perturbation and observation methods, computational tools based on optimization algorithms for low-complexity systems, and more recently, using metaheuristic algorithms for highly complex systems with improved tuning procedures that guarantee the operation and stability of the systems. Thus, avant-garde optimization algorithms that mimic the evolution of self-organizing biological systems, also called metaheuristic nature-inspired algorithms, have gained high relevance due to their great potential for solving optimization problems. Hence, the Cuckoo Search (CS) algorithm, a very promising and nearly recent developed nature-inspired algorithm, has been used in the design and optimization of Fuzzy Logic Control (FLC) systems due to its great potentiality. In particular, this chapter studies the application of the CS algorithm for tuning controller parameters in two different case studies. The first one is associated with the FLC parameter tuning of a nonlinear magnetic levitation system, and the second case study is related to the FLC optimization of the energy management system of a residential microgrid. Simulation results are provided to emphasize and analyze the features of the optimized controllers for the two cases and compared against other more conventional techniques. Obtained outcomes show that the adjustment of FLC parameters, performed through the CS algorithm, is efficient and improves the performance of the two FLC, which makes the CS algorithm becomes a powerful alternative for performing the controller parameter tuning in modern control systems.


Cuckoo search algorithm Nature-inspired algorithms Fuzzy logic control Energy management system Control systems Parameter optimization 



This work is part of the projects 2019-PIC-003-CTE from the Research Group of Propagation, Electronic Control, and Networking (PROCONET) of Universidad de las Fuerzas Armadas ESPE. This work has been partially supported by the Spanish Ministry of Industry and Competitiveness under the grants DPI2015-67292-R and FEDER ECO 1823-2015.


  1. 1.
    Arora S, Singh S (2013) A conceptual comparison of firefly algorithm, bat algorithm and Cuckoo search. In: 2013 international conference on control, computing, communication and materials (ICCCCM), pp 1–4Google Scholar
  2. 2.
    Deepthi S, Ravikumar A (2015) A study from the perspective of nature-inspired metaheuristic optimization algorithms. Int J Comput Appl 113(9):53–56Google Scholar
  3. 3.
    Alobaidi W, Sandgren E, Alkuam E (2017) Decision support through intelligent agent based simulation and multiple goal based evolutionary optimization. Intell Inf Manag 9(3):97–113Google Scholar
  4. 4.
    Fister XS, Yang J, Brest D Fister (2013) A brief review of nature-inspired algorithms for optimization. Elektrotehniski Vestnik/Electrotechnical Review 80(3):116–122Google Scholar
  5. 5.
    Muñoz J, Pantrigo MG (2007) Metaheurísticas. Dykinson, S.L, Madrid, SpainGoogle Scholar
  6. 6.
    Bhargava V, Fateen SEK, Bonilla-Petriciolet A (2013) Cuckoo Search: a new nature-inspired optimization method for phase equilibrium calculations. Fluid Phase Equilib 337:191–200Google Scholar
  7. 7.
    Teimouri R, Sohrabpoor H (2013) Application of adaptive neuro-fuzzy inference system and Cuckoo optimization algorithm for analyzing electro chemical machining process. Front Mech Eng 8(4):429–442Google Scholar
  8. 8.
    Fateen S-EK, Bonilla-Petriciolet A (2014) Unconstrained gibbs free energy minimization for phase equilibrium calculations in nonreactive systems, using an improved Cuckoo Search algorithm. Ind Eng Chem Res 53(26):10826–10834Google Scholar
  9. 9.
    Babukartik RG, Dhavachelvan P (2012) Hybrid Algorithm using the advantage of ACO and Cuckoo Search for job scheduling. Int J Inf Technol Converg Serv 2(5):51–60Google Scholar
  10. 10.
    Khadwilard A, Chansombat S, Thepphakorn T, Thapatsuwan P, Thapatsuwan W, Pongcharoen P (2012) Application of firefly algorithm and its parameter setting for job shop scheduling. J Ind Technol 8(1):11Google Scholar
  11. 11.
    Bitam S (2012) Bees life algorithm for job scheduling in cloud computing. In: The second international conference on communications and information technology, pp 186–191Google Scholar
  12. 12.
    Chandrasekaran K, Simon SP (2012) Multi-objective scheduling problem: hybrid approach using fuzzy assisted Cuckoo search algorithm. Swarm Evolut Comput 5:1–16Google Scholar
  13. 13.
    Deb K, Deb K (2014) Multi-objective optimization. Search methodologies. Springer, Boston, pp 403–449Google Scholar
  14. 14.
    Yang XS, Deb S (2013) Multiobjective Cuckoo search for design optimization. Comput Oper Res 40(6):1616–1624MathSciNetzbMATHGoogle Scholar
  15. 15.
    Berrazouane S, Mohammedi K (2014) Parameter optimization via Cuckoo optimization algorithm of fuzzy controller for energy management of a hybrid power system. Energy Convers Manag 78:652–660Google Scholar
  16. 16.
    Taruwona M, Nyirenda CN (2018) Particle Swarm optimization of a Mamdani fuzzy logic based charge controller for energy storage systems. In: 2018 open innovations conference (OI), pp 73–78Google Scholar
  17. 17.
    Truong CN, May DC, Martins R, Musilek P, Jossen A, Hesse HC (2017) Cuckoo-search optimized fuzzy-logic control of stationary battery storage systems. In: 2017 IEEE electrical power and energy conference (EPEC), pp 1–6Google Scholar
  18. 18.
    Abdelaziz Y, Ali ES (2015) Cuckoo Search algorithm based load frequency controller design for nonlinear interconnected power system. Int J Electr Power Energy Syst 73:632–643Google Scholar
  19. 19.
    Bitam S, Mellouk A, Zeadally S (2015) Bio-Inspired routing algorithms survey for vehicular ad hoc networks. IEEE Commun Surv Tutor 17(2):843–867Google Scholar
  20. 20.
    Marinakis Y, Iordanidou G-R, Marinaki M (2013) Particle Swarm optimization for the vehicle routing problem with stochastic demands. Appl Soft Comput 13(4):1693–1704Google Scholar
  21. 21.
    Saraswathi M, Murali GB, Deepak BBVL (2018) Optimal path planning of mobile robot using hybrid Cuckoo Search-Bat algorithm. Procedia Comput Sci 133:510–517Google Scholar
  22. 22.
    Mohanty PK, Parhi DR (2016) Optimal path planning for a mobile robot using cuckoo search algorithm. J Exp Theor Artif Intell 28(1):35–52Google Scholar
  23. 23.
    Mehboob U, Qadir J, Ali S, Vasilakos A (2016) Genetic algorithms in wireless networking: techniques, applications, and issues. Soft Comput 20(6):2467–2501Google Scholar
  24. 24.
    Civicioglu P, Besdok E (2013) A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif Intell Rev 39(4):315–346Google Scholar
  25. 25.
    Yang XS (2014) Nature-inspired optimization algorithms, 1st ed. Elsevier, Amsterdam, the NetherlandsGoogle Scholar
  26. 26.
    Yang X-S, Deb S (2009) Cuckoo Search via Lévy flights. In: 2009 world congress on nature & biologically inspired computing (NaBIC), pp 210–214Google Scholar
  27. 27.
    Wang T, Meskin M, Grinberg I (2017) Comparison between particle swarm optimization and Cuckoo Search method for optimization in unbalanced active distribution system. In: 2017 IEEE international conference on smart energy grid engineering (SEGE), pp 14–19Google Scholar
  28. 28.
    García-Gutiérrez G, Arcos-Aviles D, Carrera EV, Guinjoan F, Motoasca E, Ayala P, Ibarra A (2019) Fuzzy logic controller parameter optimization using metaheuristic Cuckoo Search algorithm for a magnetic levitation system. Appl Sci 9(12):2458Google Scholar
  29. 29.
    Arcos-Aviles D, García-Gutiérrez G, Guinjoan F, Carrera EV, Pascual J, Ayala P, Marroyo L, Motoasca E (2019) Adjustment of the fuzzy logic controller parameters of the energy management strategy of a grid-tied domestic electro-thermal microgrid using the Cuckoo search algorithm. In: IECON 2019—45th annual conference of the IEEE industrial electronics society, pp 115–121Google Scholar
  30. 30.
    Dey N, Ashour AS, Bhattacharyya S (eds) (2020) Applied nature-inspired computing: algorithms and case studies. Springer, SingaporeGoogle Scholar
  31. 31.
    Özdemir MT, Öztürk D, Eke Ī, Çelik V, Lee KY (2015) Tuning of optimal classical and fractional order PID parameters for automatic generation control based on the bacterial Swarm optimization. IFAC-PapersOnLine 48(30):501–506Google Scholar
  32. 32.
    Dash P, Saikia LC, Sinha N (2015) Automatic generation control of multi area thermal system using Bat algorithm optimized PD–PID cascade controller. Int J Electr Power Energy Syst 68:364–372Google Scholar
  33. 33.
    Ghaffari A, Krstic M, Seshagiri S (2014) Power optimization and control in wind energy conversion systems using extremum seeking. IEEE Trans Control Syst Technol 22(5):1684–1695Google Scholar
  34. 34.
    Song D, Fan X, Yang J, Liu A, Chen S, Joo YH (2018) Power extraction efficiency optimization of horizontal-axis wind turbines through optimizing control parameters of yaw control systems using an intelligent method. Appl Energy 224:267–279Google Scholar
  35. 35.
    Molzahn DK et al (2017) A survey of distributed optimization and control algorithms for electric power systems. IEEE Trans Smart Grid 8(6):2941–2962Google Scholar
  36. 36.
    Rajabioun R (2016) Multi-objective optimization using Cuckoo optimization algorithm: a game theory approach. Int J Acad Res Comput Eng 1(2):33–43Google Scholar
  37. 37.
    Chakraborty S, Dey N, Samanta S, Ashour AS, Barna C, Balas MM (2017) Optimization of non-rigid demons registration using Cuckoo search algorithm. Cognit Comput 9(6):817–826Google Scholar
  38. 38.
    Li Z, Dey N, Ashour AS, Tang Q (2018) Discrete cuckoo search algorithms for two-sided robotic assembly line balancing problem. Neural Comput Appl 30(9):2685–2696Google Scholar
  39. 39.
    Binh HTT, Hanh NT, Van Quan L, Dey N (2018) Improved Cuckoo search and chaotic flower pollination optimization algorithm for maximizing area coverage in wireless sensor networks. Neural Comput Appl 30(7):2305–2317Google Scholar
  40. 40.
    Mamizadeh A, Genc N, Rajabioun R (2018) Optimal tuning of PI controller for boost DC-DC converters based on Cuckoo optimization algorithm. In: 2018 7th international conference on renewable energy research and applications (ICRERA), pp 677–680Google Scholar
  41. 41.
    Wong PK, MA X, Zhao J, Xie Z, Zhao R (2017) Damping force control of a semi-active suspension system using Cuckoo Search optimized PID method. In: Proceedings of the second international conference on mechanics, materials and structural engineering (ICMMSE 2017)Google Scholar
  42. 42.
    Zhao J, Wong PK, Xie Z, Ma X, Hua X (2019) Design and control of an automotive variable hydraulic damper using Cuckoo Search optimized PID method. Int J Autom Technol 20(1):51–63Google Scholar
  43. 43.
    Puangdownreong D, Nawikavatan A, Thammarat C (2016) Optimal design of I-PD controller for DC motor speed control system by Cuckoo Search. Procedia Comput Sci 86:83–86Google Scholar
  44. 44.
    Stojanovic V, Nedic N, Prsic D, Dubonjic L, Djordjevic V (2016) Application of cuckoo search algorithm to constrained control problem of a parallel robot platform. Int J Adv Manuf Technol 87(9–12):2497–2507Google Scholar
  45. 45.
    Abd Elazim SM, Ali ES (2016) Optimal power system stabilizers design via Cuckoo Search algorithm. Int J Electr Power Energy Syst 75:99–107Google Scholar
  46. 46.
    Mehta P, Bhatt P, Pandya V (2018) Optimized coordinated control of frequency and voltage for distributed generating system using Cuckoo Search algorithm. Ain Shams Eng J 9(4):1855–1864Google Scholar
  47. 47.
    Ahmarinejad A, Hasanpour SM, Babaei M, Tabrizian M (2016) Optimal overcurrent relays coordination in microgrid using Cuckoo algorithm. Energy Procedia 100:280–286Google Scholar
  48. 48.
    Narwal A, Prasad BR (2016) A novel order reduction approach for lti systems using Cuckoo Search optimization and stability equation. IETE J Res 62(2):154–163Google Scholar
  49. 49.
    Einan M, Torkaman H, Pourgholi M (2017) Optimized fuzzy-cuckoo controller for active power control of battery energy storage system, photovoltaic, fuel cell and wind turbine in an isolated micro-grid. Batteries 3(4):23Google Scholar
  50. 50.
    Ganguly P, Kalam A, Zayegh A (2017) Optimum fuzzy logic control system design using Cuckoo Search algorithm for pitch control of a wind turbine. Advances in Modelling Analytics C 72(4):266–280Google Scholar
  51. 51.
    Fard AN, Shahbazian M, Hadian M (2016) Adaptive fuzzy controller based on cuckoo optimization algorithm for a distillation column. In 2016 international conference on computational intelligence and applications (ICCIA), pp 93–97Google Scholar
  52. 52.
    Zabihi-Samani M, Ghanooni-Bagha M (2018) An optimal Cuckoo search-fuzzy logic controller for optimal structural control. Int J Optim Civil Eng 8(1):117–135Google Scholar
  53. 53.
    Yang XS, Deb S (2014) Cuckoo search: recent advances and applications. Neural Comput Appl 24(1):169–174Google Scholar
  54. 54.
    Chechkin V, Gonchar VY, Klafter J, Metzler R (2006) Fundamentals of Lévy flight processes. Wiley, pp 439–496Google Scholar
  55. 55.
    Chechkin V, Metzler R, Klafter J, Gonchar VY (2008) Introduction to the theory of Lévy flights. Anomalous transport: foundations and applications. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany, pp 129–162Google Scholar
  56. 56.
    Mantegna RN (1994) Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes. Phys Rev E 49(5):4677–4683Google Scholar
  57. 57.
    Gutowski M (2001) Lévy flights as an underlying mechanism for global optimization algorithms. arXiv:math-ph/0106003
  58. 58.
    Wang L, Yin Y, Zhong Y (2015) Cuckoo search with varied scaling factor. Front Comput Sci 9(4):623–635Google Scholar
  59. 59.
    Checa Basantes FL (2009) Diseño e implementación de controladores clásicos y en el espacio de estados para el levitador magnético MLS. Universidad de las Fuerzas Armadas ESPEGoogle Scholar
  60. 60.
    Passino K, Yurkovich S (1998) Fuzzy control. Addisson-Wesley, Menlo Park, CAzbMATHGoogle Scholar
  61. 61.
    Chen G, Pham TT (2001) Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems, pp 316Google Scholar
  62. 62.
    Nguyen HT, Prasad NR, Walker CL, Walker EA (2002) A first course in fuzzy and neural control. Chapman & Hall/CRC PressGoogle Scholar
  63. 63.
    Marler RT, Arora JS (2010) The weighted sum method for multi-objective optimization: New insights. Struct Multidiscipl Optim 41(6):853–862MathSciNetzbMATHGoogle Scholar
  64. 64.
    Abdul Rani KN et al (2012) Modified cuckoo search algorithm in weighted sum optimization for linear antenna array synthesis. In: 2012 IEEE symposium on wireless technology and applications (ISWTA), pp 210–215Google Scholar
  65. 65.
    Lasseter RH (2002) MicroGrids. In: 2002 IEEE power engineering society winter meeting. conference proceedings (Cat. No.02CH37309), pp 305–308Google Scholar
  66. 66.
    Hatziargyriou N, Asano H, Iravani R, Marnay C (2007) Microgrids. IEEE Power Energ Mag 5(4):78–94Google Scholar
  67. 67.
    Arcos-Aviles D, Pascual J, Marroyo L, Sanchis P, Guinjoan F (2018) Fuzzy logic-based energy management system design for residential grid-connected microgrids. IEEE Trans Smart Grid 9(2):530–543Google Scholar
  68. 68.
    Arcos-Aviles D, Pascual J, Guinjoan F, Marroyo L, Sanchis P, Marietta MP (2017) Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting. Appl Energy 205:69–84Google Scholar
  69. 69.
    Arcos-Aviles D, et al (2019) A review of fuzzy-based residential grid-connected microgrid energy management strategies for grid power profile smoothing. In: Motoasca E, Kumar Agarwal A, Breesch H (eds) Energy sustainability in built and urban environments. Springer, Singapore, pp 165–199Google Scholar
  70. 70.
    Arcos-Aviles D, et al (2018) Fuzzy-based energy management of a residential electro-thermal microgrid based on power forecasting. In: IECON 2018—44th annual conference of the IEEE industrial electronics society, pp 1824–1829Google Scholar
  71. 71.
    Pascual J, Barricarte J, Sanchis P, Marroyo L (2015) Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting. Appl Energy 158:12–25Google Scholar
  72. 72.
    Pascual J, Sanchis P, Marroyo L (2014) Implementation and control of a residential electrothermal microgrid based on renewable energies, a hybrid storage system and demand side management. Energies 7(1):210–237Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  • G. García-Gutiérrez
    • 1
  • D. Arcos-Aviles
    • 1
    Email author
  • E. V. Carrera
    • 1
  • F. Guinjoan
    • 2
  • A. Ibarra
    • 1
  • P. Ayala
    • 1
  1. 1.Departamento de Eléctrica, Electrónica y TelecomunicacionesUniversidad de las Fuerzas Armadas ESPESangolquíEcuador
  2. 2.Department of Electronics EngineeringEscuela Técnica Superior de Ingenieros de Telecomunicación de Barcelona, Universitat Politècnica de CatalunyaBarcelonaSpain

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