Structural Non-Probabilistic Reliability Analysis Based on SAPSO-DE Hybrid Algorithm

Abstract:

This paper presents a structural non-probabilistic reliability analysis approach based on a hybrid algorithm of the simulated annealing-particle swarm optimization algorithm and the differential evolution (SAPSO-DE) algorithm. In the structural non-probabilistic reliability analysis, the problem with uncertain parameters can be formulated as an optimization problem using convex model. However, the limit state function is usually implicit for the uncertain parameters. By employing the SAPSO-DE hybrid algorithm based on the evolution of the cognitive and social experiences, the problem of the structural non-probabilistic reliability analysis is solved. A numerical example is given to illustrate the high precision and good feasibility of the present method. The results shows that this proposed approach is effective, and has the predominant capability of global optimization and convergence precision.

• Periodical: Applied Mechanics and Materials (Volume 551)
• Pages: 679-685
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.551.679
• Online since: May 2014
• Authors: Chan Hyok Jong, Guang Wei Meng, Feng Li*, Yan Hao, Kwan Gil Kim
• Keywords: Convex Model, Differential Evolution, Non-Probabilistic Reliability Index, Particle Swarm Optimization (PSO), Simulated Annealing (SA)

References

[1] Y. Ben-Haim, A non-probabilistic concept of reliability, Struct. Saf. 1994, 14(4): 227-245.

[2] Y. Ben-Haim, A non-probabilistic measure of reliability of linear systems based on expansion of convex models, Struct. Saf. 1995, 17(2): 91-109.

DOI: 10.1016/0167-4730(95)00004-n

[3] Y. Ben-Haim, I. Elishakoff, Discussion on: a non-probabilistic concept of reliability, Struct. Saf. 1995, 17(3): 195-199.

DOI: 10.1016/0167-4730(95)00010-2

[4] X.Z. Qiao, Y.Y. Qiu, X.G. Kong, A non-probabilistic model of structural reliability based on ellipsoidal convex model, Engineering mechanics, 2009, 26(11): 203-208.

[5] Z.J. Han, Z.Q. Wang, Structure reliability analysis base on non-probabilistic set theory, Mechanical Science and Technology for Aerospace Engineering. 2011, 30(10): 1728-1732. (In Chinese).

[6] H.J. Cao, B.Y. Duan, An approach on the non-probabilistic reliability of structures based on uncertainty convex models, Chin. J. of Comput. Mech. 2005, 22(5): 546-549. (In Chinese).

[7] Y.J. Luo, Z. Kang, An iteration approach for structural non-probabilistic reliability analysis based on hyper-ellipsoidal models, Chin. J. of Comput. Mech, 2008, 25(6): 747-752. (In Chinese).

[8] Y.J. Luo, Z. Kang, A. Li, Study on structural non-probabilistic reliability index under convex models and its solution methods, Chinese Journal of Solid Mechanics 2011, 32(6): 646-654. (In Chinese).

[9] D Moens, D. Vandepitte, A survey of non-probabilistic uncertainty treatment in finite element analysis, Computer Methods in Applied Mechanics and Engineering 2005, 194: 1527-1555.

DOI: 10.1016/j.cma.2004.03.019

[10] Z. Kang, Y. J. Luo, A. Li, On non-probabilistic reliability-based design optimization of structures with uncertain-but-bounded parameters, Struct. Saf. 2011, 33(3): 196-205.

DOI: 10.1016/j.strusafe.2011.03.002

[11] J. Kennedy, R.C. Eberhart, Particle Swarm Optimization, Proc. IEEE international conference on neural networks, November 27-December 1, (1995).

[12] M. Clerc, J. Kennedy, The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE Transactions on Evolutionary Computation 2002, 6(1): 58–73.

DOI: 10.1109/4235.985692

[13] S. Rana, S. Jasola, R. Kumar, A review on particle swarm optimization algorithms and their applications to data clustering, Artif. Intell. Rev. 2011, 35(3)211-222.

DOI: 10.1007/s10462-010-9191-9

[14] Y. Gao, S.L. Xie, Particle Swarm Optimization algorithms based on Simulated Annealing, Computer Engineering and Applications, 2004(1): 47-50. (In Chinese).

[15] V. Savsani, R.V. Rao, D.P. Vakharia, Optimal weight design of a gear train using particle swarm optimization and simulated annealing algorithms, Mechanism and Machine Theory 2010, (45): 531–541.

DOI: 10.1016/j.mechmachtheory.2009.10.010

[16] C. Zhang, J. Ning, S. Lu, D. Ouyang, T. Ding, A novel hybrid differential evolution and particle swarm optimization algorithm for unconstrained optimization, Oper. Res. Lett. 2009, 37(2): 117-122.

DOI: 10.1016/j.orl.2008.12.008

[17] M.G. Epitropakis, V.P. Plagianakos, M. N. Vrahatis, Evolving cognitive and social experience in Particle Swarm Optimization through Differential Evolution, IEEE Congress on Evolutionary Computation, July 18-July 23, (2010).

DOI: 10.1109/cec.2010.5585967