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

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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, pp. 367-386, 2022, doi: 10.1049/cje.2020.00.233

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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, pp. 367-386, 2022, doi: 10.1049/cje.2020.00.233

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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

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References(35)

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