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Enhanced crow search algorithm for AVR optimization

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Abstract

This paper proposes an enhanced crow search algorithm (ECSA) for solving numerical and real-life engineering problems. Novelties of the proposed method are fourfold: (1) addition of an archive component in the standard crow search algorithm (CSA) to incorporate past experience of finding solution (2) formulation of non-hideout position so that crow will remain near its hideout position, (3) Rechenberg’s 1/5th rule is exploited to change the flight length (instead of fixed) to speed up optimization process and (4) awareness probability is regulated to set a trade-off between local and global exploration. The performance of proposed technique is investigated on 23 benchmark functions such as unimodal, multimodal and fixed-dimension multimodal benchmark functions. The results of ECSA are compared to other state-of-the-art metaheuristic algorithms, in which ECSA outperformed other algorithms in majority of the benchmark functions. Further, to validate the effectiveness of the proposed method, ECSA has been used for optimization of proportional–integral–derivative (PID) controller. Results of ECSA–PID have been compared with conventional CSA as well as with other state-of-the-art techniques like Ziegler–Nichols (Z–N), Kitamori, ACO, multi-objective ACO, multi-objective GA and fuzzy and space gravitational optimization algorithm. The proposed algorithm is implemented on the AVR system and tested under various conditions for robustness. Consistency in the results on benchmark systems as well as on their variants and AVR system and its variants prove the robustness of the proposed method. Also, the performance of the proposed algorithm is found to be better than the existing techniques.

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References

  • Arora U, Lodhi EA, Saxena TK (2016) PID parameter tuning using modified BAT Algorithm. J Autom Control Eng 4(5):347–352. https://doi.org/10.18178/joace.4.5.347-352

    Article  Google Scholar 

  • Askarzadeh A (2016a) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers and structures. Elsevier, Amsterdam, pp 1–12. https://doi.org/10.1016/j.compstruc.2016.03.001

    Book  Google Scholar 

  • Askarzadeh A (2016b) Capacitor placement in distribution systems for power loss reduction and voltage improvement: a new methodology. IET Gener Transm Distrib 10:3631–3638

    Article  Google Scholar 

  • Askarzadeh A (2016c) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12

    Article  Google Scholar 

  • Astrom KJ, Hagglund T (1995) PID controllers: theory, design and tuning, instrument society of America, USA. 2nd Edn

  • Astrom KJ, Hagglund Y (2000) Benchmark systems for PID control. In: IFAC workshop on digital control: past, present and future of PID control. Terrassa, Spain, pp 181–182. https://doi.org/10.1016/S1474-6670(17)38238-1

  • Bagis A (2007) Determination of the PID controller parameters by modified genetic algorithm for improved performance. J Inf Sci Eng 23(5):1469–1480

    Google Scholar 

  • Bequette BW (2003) Process control: modeling, design, and simulation. Prentice Hall Inc, New Jersey

    Google Scholar 

  • Chiha I, Liouane N, Borne P (2012) Tuning PID controller using multiobjective ant colony optimization. Appl Comput Intell Soft Comput. https://doi.org/10.1155/2012/536326

    Article  Google Scholar 

  • Clayton N, Emery N (2005) Evolution of the avian brain and intelligence. Current Biol 15(23):R946–R950. https://doi.org/10.1016/j.cub.2005.11.029

    Article  Google Scholar 

  • Cohen G, Coon G (1953) Theoretical consideration of retarded control. Trans ASME 75(1):827–834

    Google Scholar 

  • Díaz P, Pérez-Cisneros M, Cuevas E, Avalos O, Gálvez J (2018) Salvador hinojosa iand daniel zaldivar an improved crow search algorithm applied to energy problems. Energies 11:571. https://doi.org/10.3390/en11030571

    Article  Google Scholar 

  • Digalakis J, Margaritis K (2001) On benchmarking functions for genetic algorithms. Int J Comput Math 77:481–506. https://doi.org/10.1080/00207160108805080

    Article  MathSciNet  MATH  Google Scholar 

  • dos Santos Coelho L, Richter C, Cocco Mariani V, Askarzadeh A (2016) Modified crow search approach applied to electromagnetic optimization. pp 6(c). https://doi.org/10.1109/CEFC.2016.7815927

  • Ekinci S, Hekimoglu B (2019) Improved kidney inspired algorithm appriach for tuning of PID controller in AVR system. IEEE Access 7:39935–39947

    Article  Google Scholar 

  • Elumalai K, Sumathi S (2017) Behavior modification of PID controller for AVR system using particle swarm optimization. ICEDSS 2017, India, 3–4 March (2017) 190–195

  • Fister D, Safaric R, Fister I Jr, Fister I (2016) Parameter tuning of PI controller with bat algorithm. INFORMATICA 109-116

  • Gaing ZL (2004) A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans Energy Convers 19(2):384–391

    Article  Google Scholar 

  • Gozde H, Taplamacioglu MC (2011) Comparative performance analysis of artificial bee colony algorithm for automatic voltage regulator (AVR) system. J Frankl Inst 348:1927–1946

    Article  Google Scholar 

  • Guptaa D, Sundarama S, Khanna A, Hassanien AE, Victor Hugo C, de Albuquerque VHC (2018) Improved diagnosis of Parkinson’s disease using optimized crow search algorithm. Comput Electr Eng 68:412–424

    Article  Google Scholar 

  • Han J, Wang P, Yang X (2012) Tuning of PID controller based on fruit fly optimization algorithm. In: International conference on mechatronics and automation. Chengdu, China: IEEE. https://doi.org/10.1109/ICMA.2012.6282878

  • Hekimoğlu B, Ekinci S (2018) Grasshopper optimization algorithm for automatic voltage regulator system. In: 5th International conference on electrical and electronics engineering. IEEE, pp 152–156. https://doi.org/10.1109/ICEEE2.2018.8391320

  • Hinojosa S, Oliva D, Cuevas E, Pajares G, Avalos O, Ga´lvez J (2018) Improving multi-criterion optimization with chaos: a novel multi-objective chaotic crow search algorithm. Neural Comput Appl 1:1. https://doi.org/10.1007/s00521-017-3251-x

    Article  Google Scholar 

  • Hsiao YT, Chuang CL, Chien CC (2004) Ant colony optimization for designing of PID controllers. In: Proceedings of 2004 IEEE international symposium on computer aided control systems design, Taipei, Taiwan, pp 321–326. https://doi.org/10.1109/CACSD.2004.1393896

  • Hsiao YT, Chuang CL, Jiang JA, Chien CC (2005) A novel optimizationalgorithm:Space Gravitational optimization. In: Proceedings of 2005 IEEE international conference on systems, man and cybernetics, Waikoloa, Hawaii, pp. 2323–2328, October 10–12. https://doi.org/10.1109/ICSMC.2005.1571495

  • Kansit S, Assawinchaichote W (2016) Optimization of PID Controller based on PSOGSA for an automatic voltage regulator system. iEECON2016, 2–4 March (2016), Thailand. https://doi.org/10.1016/j.procs.2016.05.022

  • Karimi M, Zamani M, Sadati N, Parniani M (2009) An optimal fractional order controller for an AVR system using particle swarm optimization algorithm. Control Eng Pract 17:1380–1387

    Article  Google Scholar 

  • Khalilpourazari S, Khalilpourazary S (2019) An efficient hybrid algorithm based on water cycle and moth-flame optimization algorithms for solving numerical and constrained engineering optimization problems. Soft Comput 1:1. https://doi.org/10.1007/s00500-017-2894-y

    Article  Google Scholar 

  • Kielkowicz K, Grela D (2016) Modified bat algorithm for nonlinear optimization. IJCSNS 16(10):46–50

    Google Scholar 

  • Lahcene R, Abdeldjalil S, Aissa K (2017) Optimal tuning of fractional order PID controller for AVR system using simulated annealing optimization algorithm. ICEE-B, Boumerdes, Algeria, 29–31 October

  • Lakshmi K, Karthikeyani Visalakshi N, Shanthi S (2018) Data clustering using K-Means based on crow search algorithm. Sådhanå 43:190. https://doi.org/10.1007/s12046-018-0962-3Sadhan

    Article  MathSciNet  Google Scholar 

  • Lee CH, Lee YH, Teng CC (2008) A novel robust PID controllers design by fuzzy neural network. Asian J Control 4(4):433–438. https://doi.org/10.1109/ACC.2002.1023244

    Article  Google Scholar 

  • Liu Y, Zhang J, Wang S (2004) Optimization design based on PSO algorithm for PID controller. Intell Control Autom WCICA 1:2419–2422. https://doi.org/10.1109/WCICA.2004.1342028

    Article  Google Scholar 

  • Liu D, Liu C, Fu Q, Li T, Imran K, Cui S, Abrar F (2017) ELM evaluation model of regional groundwater quality based on the crow search algorithm. Ecol Indic 81:302–314

    Article  Google Scholar 

  • Molga M, Smutnicki C (2005) Test functions for optimization needs. Science and Education Publisher, An Open Access and Academic Publisher, p 101

  • Oliva D, Hinojosa S, Cuevas E, Pajares G, Avalos O, Gálvez J (2017) Cross entropy based thresholding for magnetic resonance brain images using crow search algorithm. Expert Syst Appl 79:164–180

    Article  Google Scholar 

  • Pan I, Das S (2012) Chaotic multi-objective optimization based design of fractional order PIλDμ controller in AVR system. Int J Electr Power Energy Syst 43:393–407

    Article  Google Scholar 

  • Pillay N, Govender P (2007) A Particle swarm optimization approach for model independent tuning of PID control loops. IEEE 1:1. https://doi.org/10.1109/AFRCON.2007.4401521

    Article  Google Scholar 

  • Rajput S, Parashar M, Dubey HM, Pandit M (2016) Optimization of benchmark functions and practical problems using crow search algorithm. In: Proceedings of the 2016 5th international conference on eco-friendly computing and communication systems (ICECCS), Bhopal, India, 8–9 December 2016, pp 73–78

  • Rechenberg I (1973) Evolutions strategie-optimierung technischer systeme nach Prinzipien de rbiologischen evolution. Frommann-Holzboog, Stuttgart. https://doi.org/10.1002/fedr.19750860506

  • Sahib M, Ahmed B (2016) A new multi-objective performance criterion used in PID tuning optimization algorithms. J Adv Res 7(1):125–134

    Article  Google Scholar 

  • Sayed G, Hassanien A, Azar A (2017) Feature selection via a novel chaotic crow search algorithm. Neural Comput Appl. https://doi.org/10.1007/s00521-017-2988-6

    Article  Google Scholar 

  • Suri Babu AG, Chiranjeevi BT (2016) Implementation of fractional order PID controller for an AVR system using GA and ACO optimization techniques. IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd., Amsterdam. https://doi.org/10.1016/j.ifacol.2016.03.096

    Book  Google Scholar 

  • Varol HA, Bingul Z (2004) A new PID tuning technique using ant algorithm. In: American control conference. AACC, Boston, Massachusetts, pp 2154–2160. https://doi.org/10.23919/acc.2004.1383780

  • Xi M, Sun J, Xu W (2007) Parameter optimization of PID controller based on quantum-behaved particle swarm optimization algorithm. Complex Syst Appl Modell Control Simul 14(2):603–607. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.506.5307&rep=rep1&type=pdf

    Google Scholar 

  • Yang X-S (2010a) Appendix A: test problems in optimization. In: Engineering optimization. Wiley, Hoboken. https://doi.org/10.1002/9780470640425.app1

  • Yang XS (2010b) Nature-inspired metaheuristic algorithms. Luniver Press, London

    Google Scholar 

  • Yourui H, Xiuqiao C, Yihua H (2005) Application of iterative learning genetic algorithm for PID parameters auto- Optimization. Neural Netw Brain ICNN&B. https://doi.org/10.1109/WCICA.2006.1713006

    Article  Google Scholar 

  • Zaineb M, Alhakeem RS (2019) Ali fast channel selection method using crow search algorithm. ICICT2019’15–16 April 2019 Baghdad, Iraq, pp. 211–214

  • Zeng G, Chen J, Dai Y, Li L, Zheng C, Chen M (2015) Design of fractional order PID controller for automatic regulator voltage system based on multi-objective extremal optimization. Neurocomputing 160:173–184

    Article  Google Scholar 

  • Zhao ZY, Tomizuka M, Isaka S (1993) Fuzzy gain scheduling of PID controllers. IEEE Trans Syst Man Cybern 23:1392–1398. https://doi.org/10.1109/21.260670

    Article  Google Scholar 

  • Ziegler JG, Nichols N (1993) Optimum settings for automatic controllers. J Dyn Syst Meas Control 115(2):220

    Article  Google Scholar 

  • Zirkohi MM (2015) Optimal PID controllerdesign using adaptive VURPSO algorithm. DE Gruyter 1:179–185. https://doi.org/10.1515/eng-2015-0015

    Article  Google Scholar 

  • Zolghadr-Asli B, Bozorg-Haddad B, Xuefeng C (2018) Crow search algorithm. In: Advanced optimization by nature inspired algorithms, Chap-14. Springer, Berlin, pp 143–150

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Acknowledgements

The authors would like to thank the anonymous referees, Editor-in-chief and associate editors for their valuable time and also deeply thankful to Prof. LakhvinderKaur and Prof. Upasana.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Punjabi University, Patiala, India

    Amrit Kaur Bhullar & Ranjit Kaur

  2. Department of Electrical and Instrumentation Engineering, Thapar University, Patiala, India

    Swati Sondhi

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  1. Amrit Kaur Bhullar
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Correspondence to Swati Sondhi.

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Communicated by V. Loia.

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Bhullar, A.K., Kaur, R. & Sondhi, S. Enhanced crow search algorithm for AVR optimization. Soft Comput 24, 11957–11987 (2020). https://doi.org/10.1007/s00500-019-04640-w

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