Adaptive Simplified Chicken Swarm Optimization Based on Inverted S-Shaped Inertia Weight

GU Yanchun; LU Haiyan; XIANG Lei; SHEN Wanqiang

doi:10.1049/cje.2020.00.233

Volume 31 Issue 2

Mar. 2022

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2022 > 31(2): 367-386

GU Yanchun, LU Haiyan, XIANG Lei, et al. “Adaptive Simplified Chicken Swarm Optimization Based on Inverted S-Shaped Inertia Weight”. Chinese Journal of Electronics, vol. 31 no. 2. doi: 10.1049/cje.2020.00.233

Citation:

GU Yanchun, LU Haiyan, XIANG Lei, et al. “Adaptive Simplified Chicken Swarm Optimization Based on Inverted S-Shaped Inertia Weight”. Chinese Journal of Electronics, vol. 31 no. 2. doi: 10.1049/cje.2020.00.233

Citation:

PDF( 4224 KB)

Adaptive Simplified Chicken Swarm Optimization Based on Inverted S-Shaped Inertia Weight

doi: 10.1049/cje.2020.00.233

GU Yanchun^1
,,
LU Haiyan^{1, 2
,},
XIANG Lei¹,
SHEN Wanqiang^{1, 2
,}

1.
School of Science, Jiangnan University, Wuxi 214122, China
2.
Wuxi Engineering Technology Research Center for Biological Computing, Jiangnan University, Wuxi 214122, China

Funds: This work was supported by the National Natural Science Foundation of China (61772013) and the Natural Science Foundation of Jiangsu Province (BK20190578).

More Information

Author Bio:
was born in Shandong Province, China. She received the M.S. degree from Jiangnan University in 2021. Her main research interests is optimization and control. (Email: 1639584799@qq.com)

(corresponding author) was born in Shandong Province, China. She received the M.S. and Ph.D. degrees from Zhejiang University, China, in 1996 and 2007 respectively. Currently, she is an Associate Professor at the School of Science, Jiangnan University, China. Her research interests include combinatorial optimization, computational intelligence, and robot learning. (Email: luhaiyan@jiangnan.edu.cn)

was born in Jiangsu Province, China. She received the M.S. and Ph.D. degrees from Zhejiang University, China, in 2005 and 2010 respectively. Currently, she is an Associate Professor at the School of Science, Jiangnan University, China. Her research interests include computer aided design and computer graphics. (Email: wq_shen@163.com)
Received Date: 2020-08-06
Accepted Date: 2021-07-18

Available Online: 2021-11-03

Publish Date: 2022-03-05

Abstract

Abstract

Considering the issues of premature convergence and low solution accuracy in solving high-dimensional problems with the basic chicken swarm optimization algorithm, an adaptive simplified chicken swarm optimization algorithm based on inverted S-shaped inertia weight (ASCSO-S) is proposed. Firstly, a simplified chicken swarm optimization algorithm is presented by removing all the chicks from the chicken swarm. Secondly, an inverted S-shaped inertia weight is designed and introduced into the updating process of the roosters and hens to dynamically adjust their moving step size and thus to improve the convergence speed and solution accuracy of the algorithm. Thirdly, in order to enhance the exploration ability of the algorithm, an adaptive updating strategy is added to the updating process of the hens. Simulation experiments on 21 classical test functions show that ASCSO-S is superior to the other comparison algorithms in terms of convergence speed, solution accuracy, and solution stability. In addition, ASCSO-S is applied to the parameter estimation of Richards model, and the test results indicate that ASCSO-S has the best fitting results compared with other three algorithms.
- Chicken swarm optimization algorithm,
- Inverted S-shaped inertia weight,
- Random learning,
- Function optimization,
- Richards model

FullText(HTML)

References(35)

References

[1]	R. Cheiouah and P. Slarry, “A continuous genetic algorithm designed for the global optimization of multimodal functions,” Journal of Heuristics, vol.6, no.3, pp.191–213, 2000.
[2]	Z. J. Qu, Y. H. Chen, P. J. Li, et al., “Cooperative evolution of multiple operators based adaptive parallel quantum genetic algorithm,” Acta Electronica Sinica, vol.47, no.2, pp.266–273, 2019. (in Chinese)
[3]	J. Kenney and R. C. Eberhart, “Particle swarm optimization,” Proc. of International Conference on Neural Networks, Piscataway, New Jersey, USA, pp.1942–1948, 1995.
[4]	T. Yan, and F. X. Liu, “Quantum-behaved particle swarm optimization algorithm based on the two-body problem,” Chinese Journal of Electronics, vol.28, no.3, pp.569–576, 2019. doi: 10.1049/cje.2019.03.023
[5]	H. Sun, Z. C. Deng, J. Zhao, et al., “Hybrid mean center opposition-based learning particle swarm optimization,” Acta Electronica Sinica, vol.47, no.9, pp.1809–1818, 2019. (in Chinese)
[6]	S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Advances in Engineering Software, vol.69, no.3, pp.46–61, 2014.
[7]	S. Zhang and Y. Q. Zhou, “Grey wolf optimizer with ranking-based mutation operator for IIR model identification,” Chinese Journal of Electronics, vol.27, no.5, pp.1071–1079, 2018. doi: 10.1049/cje.2018.06.008
[8]	D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm,” Journal of Global Optimization, vol.39, no.3, pp.459–471, 2007. doi: 10.1007/s10898-007-9149-x
[9]	H. Wang, W. J. Wang, S. Y. Xiao, et al., “Improving artificial Bee colony algorithm using a new neighborhood selection mechanism,” Information Sciences, vol.64, no.3, pp.227–240, 2020.
[10]	X. B. Meng, Y. Liu, X. Z. Gao, et al, “A new bio-inspired algorithm: chicken swarm optimization,” in Proc. of International Conference on Swarm Intelligence, Hefei, China, pp.86–94, 2014.
[11]	S. Liang, T. Feng, and G. Sun, “Sidelobe-level suppression for linear and circular antenna arrays via the cuckoo search-chicken swarm optimization algorithm,” IET Microwaves Antennas & Propagation, vol.11, no.2, pp.209–218, 2017.
[12]	D. Zouache, Y. O. Arby, F. Nouioua, et al., “Multi-objective chicken swarm optimization: A novel algorithm for solving multi-objective optimization problems,” Computers & Industrial Engineering, vol.129, no.3, pp.377–391, 2019.
[13]	Y. H. Nie, C. L. Zhang, L. Gao, et al., “Research on parameter identification of static var compensator model based on improved chicken swarm optimization algorithm,” Power System Technology, vol.43, no.2, pp.731–738, 2019. (in Chinese)
[14]	F. Tian, R. Zhang, L. Jacek, et al., “Deadlock-free migration for virtual machine consolidation using Chicken Swarm Optimization algorithm,” Journal of Intelligent & Fuzzy Systems, vol.32, no.2, pp.1389–1400, 2017.
[15]	J. Cao and X. G. Qiao, “Three surfaces localization algorithm based on chicken swarm optimization for wireless sensor network,” Application Research of Computers, vol.34, no.8, pp.2483–2485, 2017. (in Chinese)
[16]	T. Shadi and F. Safi-Esfahani, “A dynamic task scheduling framework based on chicken swarm and improved raven roosting optimization methods in cloud computing,” Journal of Supercomputing, vol.74, no.6, pp.2581–2626, 2018. doi: 10.1007/s11227-018-2291-z
[17]	X. Huang, C. M. Ye, and J. Zheng, “Chicken swarm optimization algorithm of hybrid evolutionary searching strategy,” Computer Engineering and Application, vol.54, no.7, pp.176–181, 2018. (in Chinese)
[18]	M. Han, “Hybrid chicken swarm algorithm with dissipative structure and differential mutation,” Journal of Zhejiang University (Science Edition), vol.45, no.3, pp.272–283, 2018. (in Chinese)
[19]	X. D. Shi and Y. L. Gao, “Hybrid algorithm based on chicken swarm optimization and artificial bee colony,” Journal of Hefei University of Technology (Natural Science), vol.41, no.5, pp.589–594, 2018. (in Chinese)
[20]	F. Kong and D. H. Wu, “An improved chicken swarm optimization algorithm,” Journal of Jiangnan University (Natural Science Edition), vol.14, no.6, pp.681–688, 2015. (in Chinese)
[21]	F. F. Han, Q. H. Zhao, Z. H. Du, et al., “Enhanced chicken swarm algorithm for global optimization,” Application Research of Computers, vol.36, no.8, pp.2317–2319+2327, 2019. (in Chinese)
[22]	Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in Proc. of IEEE World Congress on Computational Intelligence, Anchorage, Alaska, USA, pp.69–73, 1998.
[23]	J. Q. Yang, D. M. Zhang, R. L. He, et al., “A chaotic chicken optimization algorithm based on Powell search,” Microelectronics & Computer, vol.35, no.7, pp.78–82, 2018. (in Chinese)
[24]	M. Jamil and X. S. Yang, “A literature survey of benchmark functions for global optimization problems,” International Journal of Mathematical Modelling & Numerical Optimization, vol.4, no.2, pp.150–194, 2013.
[25]	N. H. Awad, M. Z. Ali, J. J. Liang, et al, “Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective bound constrained real-parameter numerical optimization,” Technical Report, Nanyang Technological University, pp.1–16, 2016.
[26]	Y. M. Duan, H. H. Xiao, and F. Lin, “Flower pollination algorithm with new pollination methods,” Computer Engineering and Applications, vol.54, no.23, pp.94–108, 2018. (in Chinese)
[27]	F. Xue, “Research and application of heuristic intelligent optimization based on bat algorithm,” Ph.D. Thesis, Beijing University of Technology, China, 2016. (in Chinese)
[28]	W. Nie and W. Guo, “Applications of Chapman-Richards model to geotechnical engineering,” Journal of Rock Mechanics and Geotechnical Engineering, vol.11, no.6, pp.1286–1292, 2019. doi: 10.1016/j.jrmge.2018.12.019
[29]	M. L. Cheng, “Richards model parameters estimation and model application,” Mathematics in Practice and Theory, vol.40, no.12, pp.139–143, 2010. (in Chinese)
[30]	X. Y. Wang, S. Liu, and Y. Huang, “A Study on the rapid parameter estimation and the Grey prediction in Richards model,” Journal of Systems Science and Information, vol.4, no.3, pp.223–234, 2016. doi: 10.21078/JSSI-2016-223-12
[31]	G. C. White and J. T. Ratti, “Estimation and testing of parameters in Richards growth model for western grebes,” Growth, vol.41, no.4, pp.315–323, 1977.
[32]	Z. G. Yan, H. N. Hu, and G. Yang, “Parameter estimation of Richards model and algorithm effectiveness based on particle swarm optimization algorithm,” Journal of Computer Applications, vol.34, no.10, pp.2827–2830, 2014. (in Chinese)
[33]	T. F. Wu, J. B. You, M. J. Yan, et al, “Applied research of PSO in parameter estimation of Richards model,” in Proc. of 2012 Ninth Web Information Systems and Applications Conference, IEEE Computer Society, Haikou, China, pp.87–90, 2012.
[34]	J. M. Zhang, L. J. Xu, K. Zhang, et al., “Dynamic prediction of surface subsidence based on leapfrog PSO-Richards model,” Coal Technology, vol.37, no.10, pp.91–93, 2018. (in Chinese)
[35]	J. Wang, “Parameter estimation of Richards model hawed on variable step size fruit fly optimization algorithm,” Computer Engineering and Design, vol.38, no.9, pp.2402–2406, 2017. (in Chinese)