An improved spotted hyena optimizer for PID parameters in an AVR system

Guo Zhou; Jie Li; Zhonghua Tang; Qifang Luo; Yongquan Zhou; Guo Zhou; Jie Li; Zhonghua Tang; Qifang Luo; Yongquan Zhou

doi:10.3934/mbe.2020211

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 4: 3767-3783. doi: 10.3934/mbe.2020211

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An improved spotted hyena optimizer for PID parameters in an AVR system

1.
Department of Science and Technology Teaching, China University of Political Science and Law, Beijing 100088, China
2.
College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China
3.
Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence, Nanning 530006, China
4.
Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China

Received: 26 March 2020 Accepted: 17 May 2020 Published: 25 May 2020

In this paper, an improved spotted hyena optimizer (ISHO) with a nonlinear convergence factor is proposed for proportional integral derivative (PID) parameter optimization in an automatic voltage regulator (AVR). In the proposed ISHO, an opposition-based learning strategy is used to initialize the spotted hyena individual's position in the search space, which strengthens the diversity of individuals in the global searching process. A novel nonlinear update equation for the convergence factor is used to enhance the SHO's exploration and exploitation abilities. The experimental results show that the proposed ISHO algorithm performed better than other algorithms in terms of the solution precision and convergence rate.
- spotted hyena optimizer,
- opposition-based learning,
- nonlinear convergence factor,
- PID parameter optimization,
- metaheuristic
Citation: Guo Zhou, Jie Li, Zhonghua Tang, Qifang Luo, Yongquan Zhou. An improved spotted hyena optimizer for PID parameters in an AVR system[J]. Mathematical Biosciences and Engineering, 2020, 17(4): 3767-3783. doi: 10.3934/mbe.2020211

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Abstract

In this paper, an improved spotted hyena optimizer (ISHO) with a nonlinear convergence factor is proposed for proportional integral derivative (PID) parameter optimization in an automatic voltage regulator (AVR). In the proposed ISHO, an opposition-based learning strategy is used to initialize the spotted hyena individual's position in the search space, which strengthens the diversity of individuals in the global searching process. A novel nonlinear update equation for the convergence factor is used to enhance the SHO's exploration and exploitation abilities. The experimental results show that the proposed ISHO algorithm performed better than other algorithms in terms of the solution precision and convergence rate.

References

[1]	G. Huang, T. Li, Q. Lu, Artificial memory-based optimization, Syst. Eng. Theory Pract., 11 (2014), 2900-2912.
[2]	S. Rahnamayan, G. Wang, Solving large scale optimization problems by opposition-based differential evolution (ODE), WSEAS Trans. Comput., 7 (2008), 1792-1804.
[3]	R. Rao, V. Savsani, D. Vakharia, Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems, Comput. Aided Des., 43 (2011), 303-315. doi: 10.1016/j.cad.2010.12.015
[4]	E. Sayed, D. Essam, R. Sarker, S. Elsayed, Decomposition-based evolutionary algorithm for large scale constrained problems, Inf. Sci., 316 (2015), 457-486. doi: 10.1016/j.ins.2014.10.035
[5]	P. Mohapatra, K. Das, S. Roy, A modified competitive swarm optimizer for large scale optimization problems, Appl. Soft Comput., 59 (2017), 340-362. doi: 10.1016/j.asoc.2017.05.060
[6]	D. Tang, Y. Cai, J. Zhao, Y. Xue, A Quantum-behaved particle swarm optimization with memetic algorithm and memory for continuous non-linear large scale problems, Inf. Sci., 289 (2014), 162-189. doi: 10.1016/j.ins.2014.08.030
[7]	H. Wang, Z. Wu, S. Rahnamayan, Y. Liu, M. Ventresca, Enhancing particle swarm optimization using generalized opposition-based learning, Inf. Sci., 181 (2011), 4699-4714. doi: 10.1016/j.ins.2011.03.016
[8]	H. Ismkhan, Effective Heuristics for ant colony optimization to handle large-scale problems, Swarm Evol. Comput., 32 (2017), 140-149. doi: 10.1016/j.swevo.2016.06.006
[9]	Y Zhou, F. Miao, Q Luo, Symbiotic organisms search algorithm for optimal evolutionary controller tuning of fractional fuzzy controllers, Appl. Soft Comput. 77 (2019), 497-508 doi: 10.1016/j.asoc.2019.02.002
[10]	Z. Yang, Z. Chen, Z. Fan, X. Li, A Tuning of PID controller based on improved particle-swarm optimization, Control Theory Appl., 27 (2010), 1345-1352
[11]	L. Echevarría, O. Santiago, J. Fajardo, A. Silva Neto, D. Sánchez, A variant of the particle swarm optimization for the improvement of fault diagnosis in industrial systems via faults estimation, Eng. Appl. Artif. Intell., 28 (2014), 36-51. doi: 10.1016/j.engappai.2013.11.007
[12]	J. Jiang, Y. Xue, Q. Yang, Combined algorithm for PID tuning based on genetic algorithm and direct search, Comput. Simul., 12 (2005), 139-142.
[13]	Y. Zhou, J. Zhang, X. Yang, Y. Ling, Optimization of PID controller based on water wave optimization for an automatic voltage regulator system, Inf. Technol. Control, 48 (2019), 160-171.
[14]	P. B. de Moura Oliveira, E. J. S. Pires, P. Novais, Design of Posicast PID control systems using a gravitational search algorithm, Neurocomputing, 167 (2015), 18-23 doi: 10.1016/j.neucom.2014.12.101
[15]	G. Q. Zeng, J. Chen, M. R. Chen, Y. X. Dai, L. M. Li, K. D. Lu, et al., Design of multivariable PID controllers using real-coded population-based extremal optimization, Neurocomputing, 151 (2015), 1343-1353 doi: 10.1016/j.neucom.2014.10.060
[16]	A. Belkadi, H. Oulhadj, Y. Touati, S. A. Khan, B. Daachi, On the robust PID adaptive controller for exoskeletons: A particle swarm optimization based approach, Appl. Soft Comput., 60 (2017), 87-100 doi: 10.1016/j.asoc.2017.06.012
[17]	M. Gheisarnejad. An effective hybrid harmony search and cuckoo optimization algorithm based fuzzy PID controller for load frequency control, Appl. Soft Comput., 65 (2018), 121-138. doi: 10.1016/j.asoc.2018.01.007
[18]	A. Moharam, M. A. El-Hosseini, H. A. Ali, Design of optimal PID controller using hybrid differential evolution and particle swarm optimization with an aging leader and challengers, Appl. Soft Comput., 38 (2016), 727-737 doi: 10.1016/j.asoc.2015.10.041
[19]	G. Dhiman, V. Kumar, Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications, Adv. Eng. Software, 114 (2017), 48-70. doi: 10.1016/j.advengsoft.2017.05.014
[20]	N. Panda, S. K. Majhi, Improved spotted hyena optimizer with space transformational search for training pi-sigma higher order neural network, Comput. Intell., 36 (2020), 320-350. doi: 10.1111/coin.12272
[21]	H. Moayedi, D. T. Bui, D. Anastasios, B. Kalantar, Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil, Appl. Sci. Basel, 9 (2019), 2.
[22]	Q. Luo, J. Li, Y. Zhou. Spotted hyena optimizer with lateral inhibition for image matching, Multimedia Tools Appl., 78 (2019), 34277-34296. doi: 10.1007/s11042-019-08081-3
[23]	G. Dhiman, V. Kumar. Multi-objective spotted hyena optimizer: A Multi-objective optimization algorithm for engineering problems, Knowl. Based Syst., 150 (2018), 175-197. doi: 10.1016/j.knosys.2018.03.011
[24]	G. Dhiman, S. Guo, S. Kaur, ED-SHO: A framework for solving nonlinear economic load power dispatch problem using spotted hyena optimizer, Mod. Phys. Lett. A, 33 (2018), 1850239. doi: 10.1142/S0217732318502395
[25]	Y. Xu, H. Chen, J. Luo, Q. Zhang, S. Jiao, X. Zhang, Enhanced Moth-flame optimizer with mutation strategy for global optimization, Inf. Sci., 492 (2019), 181-203. doi: 10.1016/j.ins.2019.04.022
[26]	P. Hu, J. Pan, S. Chu, Improved Binary Grey Wolf Optimizer and Its application for feature selection, Knowl. Based Syst., 195 (2020), 105746. doi: 10.1016/j.knosys.2020.105746
[27]	A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, Harris hawks optimization: Algorithm and applications, Future Gener. Comput. Syst., 97 (2019), 849-872. doi: 10.1016/j.future.2019.02.028
[28]	H. Chen, Q. Zhang, J. Luo, Y. Xu, X. Zhang, An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine, Appl. Soft Comput., 86 (2020), 105884. doi: 10.1016/j.asoc.2019.105884
[29]	T. T. Nguyen, J. S. Pan, T. Dao, An improved flower pollination algorithm for optimizing layouts of nodes in wireless sensor network, IEEE Access, 7 (2019), 75985-75998. doi: 10.1109/ACCESS.2019.2921721
[30]	A. Ilany, A. Booms, K. Holekamp, Topological effects of network structure on long-term social network dynamics in a wild mammal, Ecol. Lett., 18 (2015), 687-695. doi: 10.1111/ele.12447
[31]	R. Haupt, S. Haupt, Practical genetic algorithms, second edition, New York, John Wiley & Sons, Inc. 2004.
[32]	X. Gao, X. Wang, S. J. Ovaska, K. Zenger, A hybrid optimization method of harmony search and opposition-based learning, Eng. Optim., 44 (2012), 895-914. doi: 10.1080/0305215X.2011.628387
[33]	H. Tizhoosh, Opposition-based learning: A new scheme for machine intelligence, International Conference on Intelligent Agents, IEEE, 2005,695-701. Available from: https://ieeexplore.ieee.org/abstract/document/1631345.
[34]	M. Omran, S. Al-Sharhan, Using Opposition-based learning to improve the performance of particle swarm optimization, 2008 IEEE Swarm Intelligence Symposium, 2008, 1-6. Available from: https://ieeexplore.ieee.org/abstract/document/4668288.
[35]	M. A. Ahandani, H. Alavi-Rad, Opposition-based learning in the shuffled differential evolution algorithm, Appl. Math. Comput., 16 (2012), 1303-1337.
[36]	M. Enns, Electric Energy Systems Theory, IEEE Trans. Autom. Control, 17 (1972), 749-750. doi: 10.1109/TAC.1972.1100141
[37]	H. Gozde, M. C. Taplamacioglu, Comparative performance analysis of artificial bee colony algorithm for automatic voltage regulator (AVR) system, J. Franklin Inst., 348 (2011), 1927-1946. doi: 10.1016/j.jfranklin.2011.05.012
[38]	L. Coelho, Tuning of PID Controller for an automatic regulator voltage system using chaotic optimization approach, Chaos Solitons Fractals, 39 (2009), 1504-1514. doi: 10.1016/j.chaos.2007.06.018
[39]	D. Karaboga, B. Akay, A comparative study of artificial bee colony algorithm, Appl. Math. Comput., 214 (2009), 108-132.
[40]	S. Mirjalili, S. Mirjalili, A. Lewis, Grey wolf optimizer, Adv. Eng. Software, 69 (2014), 46-61. doi: 10.1016/j.advengsoft.2013.12.007
[41]	S. Mirjalili, S. Hashim, A new hybrid PSO + GSA algorithm for function optimization International Conference on Computer and Information Application, 2010 International Conference on Computer and Information Application, 2012,374-377. Available from: https://ieeexplore.ieee.org/abstract/document/6141614.
[42]	X. Yang, Flower pollination algorithm for global optimization, International Conference on Unconventional Computing and Natural Computation, 2012,242-243. Available from: https://link.springer.com/chapter/10.1007/978-3-642-32894-7_27.
[43]	S. Mirjalili, SCA: A sine cosine algorithm for solving optimization problems, Knowl. Based Syst., 96 (2016), 120-133. doi: 10.1016/j.knosys.2015.12.022

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