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 |
[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)
|