Skip to main content
Log in

Improvisation of artificial hummingbird algorithm through incorporation of chaos theory in intelligent optimization of fractional order PID controller tuning

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Artificial hummingbird algorithm (AHA) is one of the recent bio-inspired meta-heuristic algorithms which is based on hummingbirds’ intelligent behaviours. Just like many meta-heuristic algorithms, it also suffers from freezing in local optima and slow convergence speed. In this paper, the authors have proposed a novel chaotic artificial hummingbird algorithm (ChAHA) obtained by incorporating chaos theory in the original AHA with the aim of escaping it from local minima stagnation along with high convergence rate and more precise results. Firstly, detailed studies have been performed on six different unimodal and multimodal constrained benchmark functions by employing ten different chaotic test mappings in order to determine the most enhanced and efficient one. Later, statistical testing and graphical analysis prove that incorporation of chaotic maps (especially tent map) in AHA improves the original AHA by showing promising performance. Finally, the performance of the ChAHA (with tent map) is also validated by finding the optimum gain values of a fractional order proportional-integral-derivative (FOPID) controller, meticulously tailored to meet the specific requirements of DC motor speed control in MATLAB/Simulink. It has been unambiguously affirmed that the closed loop system with the proposed ChAHA-FOPID controller has better performance than certain pre-existing controllers such as grey wolf optimization based FOPID (GWO-FOPID), atom search optimization based FOPID (ASO-FOPID) and manta ray foraging optimization based FOPID (MRFO-FOPID) controllers. Finally, robustness analysis is also carried out with parameter variations of DC motor and the final simulation results validate the superiority of the proposed approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data availability

All data generated or analyzed during this work are included in this published article.

References

  1. Yang X-S (2008) Introduction to mathematical optimization: from linear programming to metaheuristics. Cambridge International Science Publishing, Cambridge

    Google Scholar 

  2. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82. https://doi.org/10.1109/4235.585893

    Article  Google Scholar 

  3. Yang X-S (2010) Engineering optimization: an introduction with metaheuristic applications. Wiley, Cambridge

    Book  Google Scholar 

  4. Zhao W, Wang L, Mirjalili S (2022) Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications. Comput Methods Appl Mech Eng 388:1141940. https://doi.org/10.1016/j.cma.2021.114194

    Article  MathSciNet  Google Scholar 

  5. Shadman Abid M, Apon HJ, Morshed KA, Ahmed A (2022) Optimal planning of multiple renewable energy-integrated distribution system with uncertainties using artificial hummingbird algorithm. IEEE Access 10:40716–40730. https://doi.org/10.1109/ACCESS.2022.3167395

    Article  Google Scholar 

  6. Haddad S, Lekouaghet B, Benghanem M, Soukkou A, Rabhi A (2022) Parameter estimation of solar modules operating under outdoor operational conditions using artificial hummingbird algorithm. IEEE Access 10:51299–51314. https://doi.org/10.1109/ACCESS.2022.3174222

    Article  Google Scholar 

  7. Alamir N, Kamel S, Megahed TF, Hori M, Abdelkader SM (2022) Developing an artificial hummingbird algorithm for probabilistic energy management of microgrids considering demand response. Front Energy Res 10:905788. https://doi.org/10.3389/fenrg.2022.905788

    Article  Google Scholar 

  8. Fathy A (2022) A novel artificial hummingbird algorithm for integrating renewable based biomass distributed generators in radial distribution systems. Appl Energy 323:119605. https://doi.org/10.1016/j.apenergy.2022.119605

    Article  Google Scholar 

  9. Wang J, Li Y, Hu G, Yang M (2022) An enhanced artificial hummingbird algorithm and its application in truss topology engineering optimization. Adv Eng Inform 54:101761. https://doi.org/10.1016/j.aei.2022.101761

    Article  Google Scholar 

  10. Ramadan A, Kamel S, Hassan MH, Ahmed EM, Hasanien HM (2022) Accurate photovoltaic models based on an adaptive opposition artificial hummingbird algorithm. Electronics 11(3):318. https://doi.org/10.3390/electronics11030318

    Article  Google Scholar 

  11. Ali MAS, FathimathulSalama Abd Elminaam PD (2022) A feature selection based on improved artificial hummingbird algorithm using random opposition-based learning for solving waste classification problem. Mathematics 10(15):2675. https://doi.org/10.3390/math10152675

    Article  Google Scholar 

  12. Elaziz MA, Dahou A, El-Sappagh S, Mabrouk A, Gaber MM (2022) AHA-AO: artificial hummingbird algorithm with Aquila optimization for efficient feature selection in medical image classification. Appl Sci 12(19):9710. https://doi.org/10.3390/app12199710

    Article  Google Scholar 

  13. Sarhana S, Shaheen A, El-Sehiemy R, Gafar M (2023) Optimal multi-dimension operation in power systems by an improved artificial hummingbird optimizer. Hum Centric Comput Inf Sci 1:3. https://doi.org/10.22967/HCIS.2023.13.013

    Article  Google Scholar 

  14. Yildiz BS, Mehta P, Sait SM, Panagant N, Kumar S, Yildiz AR (2022) A new hybrid artificial hummingbird-simulated annealing algorithm to solve constrained mechanical engineering problems. Mater Test 64(7):1043–1050. https://doi.org/10.1515/mt-2022-0123

    Article  Google Scholar 

  15. Emam MM, Houssein EH, Tolba MA, Zaky MM, Hamouda Ali M (2023) Application of modified artificial hummingbird algorithm in optimal power flow and generation capacity in power networks considering renewable energy sources. Sci Rep 13(1):21446. https://doi.org/10.1038/s41598-023-48479-6

    Article  Google Scholar 

  16. Alhumade H, Houssein EH, Rezk H, Moujdin IA, Al-Shahrani S (2023) Modified artificial hummingbird algorithm-based single-sensor global MPPT for photovoltaic systems. Mathematics 11(4):979. https://doi.org/10.3390/math11040979

    Article  Google Scholar 

  17. Zelinka I, Chen G (2010) Motivation for application of evolutionary computation to chaotic systems. In: Evolutionary algorithms and chaotic systems, pp 3–36. https://doi.org/10.1007/978-3-642-10707-8_1

  18. Pecora LM, Carroll TL (1990) Synchronization in chaotic systems. Phys Rev Lett 64(8):821. https://doi.org/10.1103/PhysRevLett.64.821

    Article  MathSciNet  Google Scholar 

  19. Yang D, Li G, Cheng G (2007) On the efficiency of chaos optimization algorithms for global optimization. Chaos Solitons Fractals 34(4):1366–1375. https://doi.org/10.1016/j.chaos.2006.04.057

    Article  MathSciNet  Google Scholar 

  20. Kohli M, Arora S (2018) Chaotic grey wolf optimization algorithm for constrained optimization problems. J Comput Des Eng 5(4):458–472. https://doi.org/10.1016/j.jcde.2017.02.005

    Article  Google Scholar 

  21. Ahmad M, Alam MZ, Umayya Z, Khan S, Ahmad F (2018) An image encryption approach using particle swarm optimization and chaotic map. Int J Inf Technol 10:247–255. https://doi.org/10.1007/s41870-018-0099-y

    Article  Google Scholar 

  22. Misaghi M, Yaghoobi M (2019) Improved invasive weed optimization algorithm (IWO) based on chaos theory for optimal design of PID controller. J Comput Des Eng 6(3):284–295. https://doi.org/10.1016/j.jcde.2019.01.001

    Article  Google Scholar 

  23. Arora S, Anand P (2019) Chaotic grasshopper optimization algorithm for global optimization. Neural Comput Appl 31:4385–4405. https://doi.org/10.1007/s00521-018-3343-2

    Article  Google Scholar 

  24. Sayed GI, Khoriba G, Haggag MH (2018) A novel chaotic salp swarm algorithm for global optimization and feature selection. Appl Intell 48:3462–3481. https://doi.org/10.1007/s10489-018-1158-6

    Article  Google Scholar 

  25. Kaur G, Arora S (2018) Chaotic whale optimization algorithm. J Comput Des Eng 5(3):275–284. https://doi.org/10.1016/j.jcde.2017.12.006

    Article  Google Scholar 

  26. Arora S, Singh S (2017) An improved butterfly optimization algorithm with chaos. J Intell Fuzzy Syst 32(1):1079–1088. https://doi.org/10.3233/JIFS-16798

    Article  Google Scholar 

  27. Saremi S, Mirjalili S, Lewis A (2014) Biogeography-based optimisation with chaos. Neural Comput Appl 25:1077–1097. https://doi.org/10.1007/s00521-014-1597-x

    Article  Google Scholar 

  28. Verma AS, Choudhary A, Tiwari S (2023) A novel chaotic archimedes optimization algorithm and its application for efficient selection of regression test cases. Int J Inf Technol 15(2):1055–1068. https://doi.org/10.1007/s41870-022-01031-7

    Article  Google Scholar 

  29. Shinde V, Jha R, Mishra DK (2023) Improved Chaotic Sine Cosine Algorithm (ICSCA) for global optima. Int J Inf Technol. https://doi.org/10.1007/s41870-023-01537-8

    Article  Google Scholar 

  30. Bansal B, Sahoo A (2022) Chaotic driven gorilla troops optimizer based NMF approach for integrative analysis of multiple source data. Int J Inf Technol 14(7):3437–3448. https://doi.org/10.1007/s41870-022-00928-7

    Article  Google Scholar 

  31. Alam A, Muqeem M (2023) An optimal heart disease prediction using chaos game optimization-based recurrent neural model. Int J Inf Technol. https://doi.org/10.1007/s41870-023-01597-w

    Article  Google Scholar 

  32. Mirjalili S, Gandomi AH (2017) Chaotic gravitational constants for the gravitational search algorithm. Appl Soft Comput 53:407–419. https://doi.org/10.1016/j.asoc.2017.01.008

    Article  Google Scholar 

  33. Kaveh A, Kaveh A (2017) Chaos embedded metaheuristic algorithms. In: Advances in metaheuristic algorithms for optimal design of structures, pp 375–398. https://doi.org/10.1007/978-3-319-46173-1_12

  34. Shah P, Agashe S (2016) Review of fractional PID controller. Mechatronics 38:29–41. https://doi.org/10.1016/j.mechatronics.2016.06.005

    Article  Google Scholar 

  35. Izci D, Ekinci S (2023) Fractional order controller design via gazelle optimizer for efficient speed regulation of micromotors. e-Prime-Adv Electr Eng Electron Energy 6:100295. https://doi.org/10.1016/j.prime.2023.100295

    Article  Google Scholar 

  36. Izci D, Ekinci S, Zeynelgil HL, Hedley J (2021) Fractional order PID design based on novel improved slime mould algorithm. Electr Power Compon Syst 49(9–10):901–918. https://doi.org/10.1080/15325008.2022.2049650

    Article  Google Scholar 

  37. Agarwal J, Parmar G, Gupta R, Sikander A (2018) Analysis of grey wolf optimizer based fractional order PID controller in speed control of DC motor. Microsyst Technol 24:4997–5006. https://doi.org/10.1007/s00542-018-3920-4

    Article  Google Scholar 

  38. Hekimoğlu B (2019) Optimal tuning of fractional order PID controller for DC motor speed control via chaotic atom search optimization algorithm. IEEE Access 7:38100–38114. https://doi.org/10.1109/ACCESS.2019.2905961

    Article  Google Scholar 

  39. Ekinci S, Izci D, Hekimoğlu B (2021) Optimal FOPID speed control of DC motor via opposition-based hybrid manta ray foraging optimization and simulated annealing algorithm. Arab J Sci Eng 46(2):1395–1409. https://doi.org/10.1007/s13369-020-05050-z

    Article  Google Scholar 

  40. Tavazoei MS, Haeri M (2007) Comparison of different onedimensional maps as chaotic search pattern in chaos optimization algorithms. Appl Math Comput 187(2):1076–1085. https://doi.org/10.1016/j.amc.2006.09.087

    Article  MathSciNet  Google Scholar 

  41. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18. https://doi.org/10.1016/j.swevo.2011.02.002

    Article  Google Scholar 

  42. García S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. J Heurist 15:617–644. https://doi.org/10.1007/s10732-008-9080-4

    Article  Google Scholar 

  43. Wilcoxon F (1992) Individual comparisons by ranking methods. In: Breakthroughs in statistics: methodology and distribution. Springer New York, New York, pp 196–202. https://doi.org/10.1007/978-1-4612-4380-9_16

  44. Sarma H, Bardalai A (2023) Tuning of PID controller using driving training-based optimization for speed control of DC motor. In: 2023 4th international conference on computing and communication systems (I3CS), pp 1–8. https://doi.org/10.1109/I3CS58314.2023.10127458

Download references

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hrishikesh Sarma.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this manuscript.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sarma, H., Bardalai, A. Improvisation of artificial hummingbird algorithm through incorporation of chaos theory in intelligent optimization of fractional order PID controller tuning. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-024-01791-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41870-024-01791-4

Keywords

Navigation