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| Parametric identification for nonlinear response model of a stranded wire helical spring based on improved reverse learning difference evolution Algorithm |
| DING Chuanjun, ZHANG Xiangyan, LIU Ning |
| School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China |
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Abstract Aiming at shortcomings of traditional algorithms in identifying the response model parameters of a stranded wire helical spring, an improved reverse difference evolution algorithm was proposed.A novel reverse learning strategy was used to guide populations approaching the global optimal solution.In order to improve populations’ diversity, the sinusoidal chaotic sequence was used to calculate the scaling factor.These two mechanisms cooperatively operated to better balance the global exploration ability and the local mining one of the proposed algorithm.Through establishing the objective function of parametric identification and conducting dynamic tests of a stranded wire helical spring, the improved algorithm was used to optimize the objective function, and obtain the model parameters.The results showed that the proposed algorithm can effectively recognize the response model parameters of the stranded wire helical spring; the parameters’ convergence speed and the success rate of computation are superior to those of the standard reverse difference evolution algorithm and other algorithms; even in the case of higher noise level, the proposed algorithm can correctly recognize model parameters of stranded wire helical springs.
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Received: 04 May 2017
Published: 28 December 2018
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| Cite this article: |
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DING Chuanjun,ZHANG Xiangyan,LIU Ning. Parametric identification for nonlinear response model of a stranded wire helical spring based on improved reverse learning difference evolution Algorithm[J]. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(1): 187-194.
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| URL: |
| http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2019/V38/I1/187 |
[1] 王时龙, 周杰, 李小勇, 等. 多股螺旋弹簧[M].北京: 科学出版社, 2011: 47-51.
WANG Shi-long, ZHOU Jie, LI Xiao-yong, et al. Stranded Wire Helical Spring[M]. Beijing: Science Press, 2011: 47-51.
[2] 闵建军, 王时龙. 多股螺旋弹簧动态计算分析[J].机械工程学报, 2007, 43(3): 199-203.
MIN Jian-jun, WANG Shi-long. Dynamic Design Method for Stranded Wire Helical Spring [J]. Journal of Mechanical Engineering, 2007, 43(3): 199-203.
[3] Zhao Y, Wang S, Zhou J, et al. Three-stage method for identifying the dynamic model parameters of stranded wire helical springs[J]. Chinese Journal of Mechanical Engineering, 2014, 28(1): 197-207.
[4] Das S, Mullick S S, Suganthan P N. Recent advances in differential evolution: An updated survey[J]. Swarm and Evolutionary Computation, 2016, 27: 1-30.
[5] Quaranta G, Marano G C, Greco R, et al. Parametric identification of seismic isolators using differential evolution and particle swarm optimization[J]. Applied Soft Computing, 2014, 22: 458-464.
[6] Worden K, Manson G. On the identification of hysteretic systems. Part I: Fitness landscapes and evolutionary identification[J]. Mechanical Systems and Signal Processing, 2012, 29: 201-212.
[7] Wang G, Chen G Q, Bai F Z. Modeling and identification of asymmetric Bouc-Wen hysteresis for piezoelectric actuator via a novel differential evolution algorithm[J]. Sensors And Actuators a-Physical, 2015, 235: 105-118.
[8] Rahnamayan S, Tizhoosh H R, Salama M M A. Opposition-based differential evolution for optimization of noisy problems [C] // IEEE Congress on Evolutionary Computation. Vancouver, BC, Canada: Inst. of Elec. and Elec. Eng. Computer Society. 2006. 1865-1872.
[9] Wang H, Wu Z J, Rahnamayan S, et al. Using opposition-based learning to enhance differential evolution: A comparative study [C] //IEEE Congress on Evolutionary Computation. Vancouver, BC, Canada: Institute of Electrical and Electronics Engineers Inc. 2016. 71-77.
[10] Rahnamayan S, Tizhoosh H R, Salama M M A. Quasi-oppositional differential evolution [C] // IEEE Congress on Evolutionary Computation. Singapore: Inst. of Elec. and Elec. Eng. Computer Society. 2007. 2229-2236.
[11] Ergezer M, Simon D, Du D. Oppositional biogeography-based optimization [C] // Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. San Antonio, TX, United states: Institute of Electrical and Electronics Engineers Inc. 2009. 1009-1014.
[12] Xu Q, Guo L, Wang N, et al. A novel oppositional biogeography-based optimization for combinatorial problems [C] // 10th International Conference on Natural Computation. Xiamen, China: Institute of Electrical and Electronics Engineers Inc. 2014. 412-418.
[13] Rahnamayan S, Jesuthasan J, Bourennani F, et al. Computing opposition by involving entire population [C] // Proceedings of the 2014 IEEE Congress on Evolutionary Computation. Beijing, China: Institute of Electrical and Electronics Engineers Inc. 2014. 1800-1807.
[14] Seif Z, Ahmadi M B. An opposition-based algorithm for function optimization[J]. Engineering Applications Of Artificial Intelligence, 2015, 37: 293-306.
[15] Kushida J I, Hara A, Takahama T. An improvement of opposition-based differential evolution with archive solutions [C] // International Conference on Advanced Mechatronic Systems. Kumamoto, Japan: IEEE Computer Society. 2014. 463-468.
[16] Wu Y, Zhao B, Guo J. A fast opposition-based differential evolution with cauchy mutation [C] // Proceedings-2012 3rd Global Congress on Intelligent Systems. Wuhan, China: IEEE Computer Society. 2012. 72-75.
[17] Salehinejad H, Rahnamayan S, Tizhoosh H R. Micro-differential evolution: Diversity enhancement and a comparative study[J]. Applied Soft Computing, 2017, 52: 812-833.
[18] Chelliah T R, Thangaraj R, Allamsetty S, et al. Coordination of directional overcurrent relays using opposition based chaotic differential evolution algorithm[J]. International Journal Of Electrical Power & Energy Systems, 2014, 55: 341-350.
[19] Kumar S, Sharma V K, Kumari R, et al. Opposition based levy flight search in differential evolution algorithm [C] // 2014 International Conference on Signal Propagation and Computer Technology. Ajmer, India: IEEE Computer Society. 2014. 361-367.
[20] Gao W F, Liu S Y, Huang L L. Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique[J]. Communications In Nonlinear Science And Numerical Simulation, 2012, 17(11): 4316-4327.
[21] Xin Z, Shiu Yin Y. Opposition-based adaptive differential evolution [C] // IEEE Congress on Evolutionary Computation. Brisbane, QLD, Australia: IEEE Computer Society. 2012: 1-8.
[22] Ikhouane F, Gomis-Bellmunt O. A limit cycle approach for the parametric identification of hysteretic systems[J]. Systems & Control Letters, 2008, 57(8): 663-669.
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