Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (6): 1317-1326.doi: 10.21629/JSEE.2018.06.19

• Reliability • Previous Articles     Next Articles

Structural reliability analysis using enhanced cuckoo search algorithm and artificial neural network

Qiang QIN1,*(), Yunwen FENG2(), Feng LI1,3()   

  1. 1 Aerospace Science and Industry Corporation Defense Technology Research and Test Center, Beijing 100854, China
    2 School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China
    3 CASIC Space Engineering Development Co., Ltd, Beijing 100854, China
  • Received:2018-01-19 Online:2018-12-25 Published:2018-12-26
  • Contact: Qiang QIN E-mail:johnnystyle@126.com;fengyunwen@nwpu.edu.cn;15210164875@163.com
  • About author:QIN Qiang was born in 1986. He received his B.S. degree in safety engineering, M.S. degree and Ph.D. degree in aircraft design from Northwestern Polytechnical University in 2009, 2012 and 2016, respectively. He is now an engineer in Aerospace Science and Industry Corporation Defense Technology Research and Test Center. His research interests are meta-heuristic algorithms and structural reliability. He has published four research papers about the application of the cuckoo search algorithm. E-mail: johnnystyle@126.com|FENG Yunwen was born in 1968. She received her Ph.D. degree in School of Aeronautics from Northwestern Polytechnical University, Xi'an, China, in 2000. Now she is a professor of this university. Her current research interests include structural and mechanical reliability. E-mail: fengyunwen@nwpu.edu.cn|LI Feng was born in 1991. He received his B.S. degree and M.S. degree in solid mechanics from Northwestern Polytechnical University in 2014 and 2017, respectively. He is now an engineer in CASIC Space Engineering Development Co., Ltd. His research interest is structural optimization analysis. E-mail: 15210164875@163.com
  • Supported by:
    the National Natural Science Foundation of China(51875465);This work was supported by the National Natural Science Foundation of China (51875465)

Abstract:

The present study proposed an enhanced cuckoo search (ECS) algorithm combined with artificial neural network (ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search (CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step size α0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.

Key words: structural reliability, enhanced cuckoo search (ECS), artificial neural network (ANN), cuckoo search (CS) algorithm