Genetic algorithms-aided reliability analysis

• Autorzy: Harnpornchai N.
• Języki publikacji: EN

Abstrakty

EN

A hybrid procedure of Genetic Algorithms (GAs) and reliability analysis is described, discussed, and summarized. The procedure is specifically referred to as a Genetic Algorithms-aided (GAs-aided) reliability analysis. Two classes of GAs, namely simple GAs and multimodal GAs, are introduced to solve a number of important problems in reliability analysis. The problems cover the determination of Point of Maximum Likelihood in failure domain (PML), the computation of failure probability using the GAs-determined PML, and the determination of multiple design points. The MCS-based method using the GAs-determined PML is specifically implemented in the so-called an Importance Sampling around PML (ISPML). The application of GAs to each respective problem is then demonstrated via numerical examples in order to clarify the procedures. With an aid from GAs, reliability analysis is possible even if there is no information about the geometry or landscape of limit state surfaces and the total number of crucial likelihood points. In addition, GAs significantly improve the computational efficiency and realize the analysis of rare events under constrained computational resources. The implementation of GAs to reliability analysis for building up the hybrid procedure is readily because of their algorithmic simplicity.

Słowa kluczowe

EN

genetic algorithms; reliability analysis; simulation methods; complex systems; multiple failure modes

• Wydawca: Polskie Towarzystwo Bezpieczeństwa i Niezawodności
• Czasopismo: Journal of Polish Safety and Reliability Association
• Rocznik: 2009
• Tom: Vol. 1
• Strony: 145--156
• Opis fizyczny: Bibliogr. 21 poz., rys., tab., wykr.

Twórcy

autor

Harnpornchai N.

• College of Arts, Media, and Technology, Chiang Mai University, Chiang Mai, Thailand

Bibliografia

• [1] Barbosa, H. & Lemonge, A. (2003). A new adaptive penalty scheme for genetic algorithms. Information sciences, 156, 215-251.
• [2] Brownlee, J. (2004). Parallel Niching Genetic Algorithms: A Crowding Perspective. Thesis in Master of Information Technology, Centre for Intelligent Systems and Complex Processes, School of Information Technology, Swinburne University of Technology, Australia.
• [3] Darwin, Ch. (1869). On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life. 5th ed., John Murray, London.
• [4] Deb, K. (1995). Optimization for Engineering Design Algorithms and Examples. Prentice-Hall, New York.
• [5] De Jong, K. A. (1975). An analysis of the behawior of a class of genetic adaptive systems. Ph.D. Thesis, Department of Computer Science, University of Michigan, Ann Arbor, MI, USA.
• [6] Fishman, G. (1996). Monte Carlo: Concepts, Algorithms, and Applications. Springer-Verlag, New York.
• [7] Gen, M. & Cheng, R. (1997). Genetic algorithms and engineering design. John Wiley and Sons, New York.
• [8] Gen, M. & Yun, Y.S. (2006). Soft computing approach for reliability optimization: State-of-theart survey. Reliability Engineering and System Safety, 91, 1008-1026.
• [9] Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Massachusetts, Addison-Wesley, 1989.
• [10] Harnpornchai, N., Chakpitak, N., Chandarasupsang, T., Tuang-Ath Chaikijkosi. & Dahal, K. (2007). Dynamic adjustment of age distribution in Human Resource Management by genetic algorithms. Evolutionary Computation, CEC 2007, IEEE Congress on 25-28 Sept. 2007, 1234-1239.
• [11] Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, Michigan.
• [12] Kimura, M. (1983). The Neutral Theory of Molecular Evolution. Cambridge University Press.
• [13] Mahfoud, S. W. (1995). A comparison of paralel and sequential niching methods. International Conference on Genetic Algorithms, 136-143.
• [14] Mahfoud, S. W. (1995). Niching methods for genetic algorithms. Ph.D. dissertation, Univ. of Illinois, Urbana-Champaign.
• [15] Michalewicz, A. (1996). Genetic + Data Structures = Evolution Programs. Second ed. Berlin, Springer.
• [16] Miller, B. L & Shaw, M. J. (1995). Genetic algorithms with dynamic niche sharing for multimodal function optimization. IlliGAL Report no. 9510.
• [17] Obadage, A. S. & Hampornchai, N. (2006). Determination of point of maximum likelihood in failure domain using genetic algorithms. Int J Press Vessels Pipe, 83 (4), 276-82.
• [18] Qing, L., Gang, W., Zaiyue, Y. & Qiuping, W. (2008). Crowding clustering genetic algorithm for multi-modal function optimization,. International Journal of Applied Soft Computing, Volume 8, Issue 1, 88-95.
• [19] Sprung, I. (2003). Invariance of safety factor in probabilistic fracture mechanics analysis. Int J Press Vessels Pipe, 80, 367–378.
• [20] Tada, H. (1978). The stress analysis of cracks handbook. Del Research Corporation.
• [21] Wallin, K. (1984). The scatter in KIc results. Engng Fract Mech,19, 1085-93.