Application of multiple-population genetic algorithm in parameter identification for PBX constitutive model

Gao Jun; Huang Zaixing

doi:10.11883/1001-1455(2016)06-0861-08

Volume 36 Issue 6

Oct. 2018

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Gao Jun, Huang Zaixing. Application of multiple-population genetic algorithm in parameter identification for PBX constitutive model[J]. Explosion And Shock Waves, 2016, 36(6): 861-868. doi: 10.11883/1001-1455(2016)06-0861-08

Citation:

Gao Jun, Huang Zaixing. Application of multiple-population genetic algorithm in parameter identification for PBX constitutive model[J]. Explosion And Shock Waves, 2016, 36(6): 861-868. doi: 10.11883/1001-1455(2016)06-0861-08

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Application of multiple-population genetic algorithm in parameter identification for PBX constitutive model

doi: 10.11883/1001-1455(2016)06-0861-08

Gao Jun^1
,,
Huang Zaixing²

1.
Shanghai Civil Aviation College, Shanghai 200232, China
2.
State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, Jiangsu, China

Received Date: 2015-01-27
Rev Recd Date: 2015-05-28
Publish Date: 2016-11-25

Abstract

Abstract

In this work, the standard genetic algorithm (SGA) was parallel processed using multiple parallel structures. Based on the structures, a multiple-population genetic algorithm (MPGA) was established by introducing the immigration operator and quintessence population. Self-adaptive operators of crossover probability and mutation probability were designed to improve the convergence speed of the MPGA. Combining ABAQUS with the improved multiple-population genetic optimized algorithm, a parameter identification method of constitutive model was built. Using the proposed method, a simulation example of parameter identification for PBX viscoelastic damage constitutive model was carried out. Comparison was made between methods based on SGA and MPGA. The results show that the MPGA method can effectively overcome the difficulty of the premature convergence and the identification result is more robust. The method is suitable for the optimization of complex nonlinear systems due to its superiority in the convergence speed and searching ability, and it can be applied to the parameter identification of other models.
- solid mechanics,
- parameter identification,
- multiple-population genetic algorithm,
- PBX explosive,
- ABAQUS,
- constitutive model

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References(16)

References

[1]	梁增友.炸药冲击损伤与起爆特性[M].北京:电子工业出版社, 2009.
[2]	郭虎, 罗景润.循环载荷下PBX力学行为研究[J].爆炸与冲击, 2013, 33(增刊1): 105-110. http://www.cnki.com.cn/Article/CJFDTOTAL-BZCJ2013S1019.htm Guo Hu, Luo Jingrun. Mechanical behavior of PBX under cyclic loadings[J]. Explosion and Shock Waves, 2013, 33(Suppl 1):105-110. http://www.cnki.com.cn/Article/CJFDTOTAL-BZCJ2013S1019.htm
[3]	Rauchs G, Bardon J. Identification of elasto-viscoplastic material parameters by indentation testing and combined finite element modelling and numerical optimization[J]. Finite Elements in Analysis and Design, 2011, 47(7):653-667. doi: 10.1016/j.finel.2011.01.008
[4]	Springmann M, Kuna M. Identification of material parameters of the Gurson-Tvergaard-Needleman model by combined experimental and numerical techniques[J]. Computational Materials Science, 2005, 32(3/4):544-552. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=f907b29be395d8906bcc03bac71e5e48
[5]	Chaparro B M, Thuillier S, Menezes L F, et al. Material parameters identification: Gradient-based, genetic and hybrid optimization algorithms[J]. Computational Materials Science, 2008, 44(2):339-346. doi: 10.1016/j.commatsci.2008.03.028
[6]	Majzoobi G H, Dehgolan F R. Determination of the constants of damage models[J]. Procedia Engineering, 2011, 10:764-773. doi: 10.1016/j.proeng.2011.04.127
[7]	Muñoz-Rojas P A, Cardoso E L, Vaz M. Parameter identification of damage models using genetic algorithms[J]. Experimental Mechanics, 2010, 50(5):627-634. doi: 10.1007/s11340-009-9321-y
[8]	陈炳瑞, 冯夏庭, 丁秀丽, 等.基于模式-遗传-神经网络的流变参数反演[J].岩石力学与工程学报, 2005, 24(4): 553-558. doi: 10.3321/j.issn:1000-6915.2005.04.002 Chen Bingrui, Feng Xiating, Ding Xiuli, et al. Back analysis on rheological parameters based on pattern-genetic-neural network[J]. Chinese Journal of Rock Mechanics and Engineering, 2005, 24(4):553-558. doi: 10.3321/j.issn:1000-6915.2005.04.002
[9]	高军, 黄再兴. PBX炸药粘弹性损伤本构模型的参数识别[J].工程力学, 2013, 30(7): 299-304. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gclx201307045 Gao Jun, Huang Zaixing. Parameter identification for viscoelastic damage constitutive model of PBX[J]. Engineering Mechanics, 2013, 30(7): 299-304. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gclx201307045
[10]	Solano G J, Rodriguez V K, Garcia N D. Model-based spectral estimation of Doppler signals using parallel genetic algorithms[J]. Artificial Intelligence in Medicine, 2000, 19(1):75-89. doi: 10.1016/S0933-3657(99)00051-2
[11]	李守巨, 刘迎曦, 孙伟.智能计算与参数反演[M].北京:科学出版社, 2008.
[12]	Cantu-Paz E. Designing efficient and accurate parallel genetic algorithms (parallel algorithms)[D]. University of Illinois at Urbana-Champaign, 1999. http://link.springer.com/978-1-4615-4369-5
[13]	Potts J C, Giddens T D, Yadav S B. The development and evaluation of an improved genetic algorithm based on migration and artificial selection[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1994, 24(1):73-86. doi: 10.1109/21.259687
[14]	刘桂萍.基于微型遗传算法的多目标优化方法及应用研究[D].长沙: 湖南大学, 2008. http://cdmd.cnki.com.cn/Article/CDMD-10532-2008081553.htm
[15]	Sheblé G B, Brittig K. Refined genetic algorithm-economic dispatch example[J]. IEEE Transactions on Power Systems, 1995, 10(1):117-124. doi: 10.1109/59.373934
[16]	Paris P C, Erdogan F. A critical analysis of crack propagation laws[J]. Journal of Fluids Engineering, 1963, 85(4):528-533.