Abstract
In our day-to-day life, a system never works on the conditions specified while it was being designed. Hence, it becomes necessary to include the uncertainties present in these systems while obtaining the optimized conditions. These uncertainties present in a reliability model can be handled well if the probabilistic constraints of such models are satisfied. This paper is directed to handle these probabilistic constraints of a reliability-based design optimization model (RBDO). For this purpose, one of the most efficient and precise optimization tools, namely genetic algorithm (GA), has been used. The basic principle of GAs involves the combination of the fittest string structures which are generated through numerous random iterations. The main objective of this paper is to incorporate the efficient function of a multi-objective evolutionary algorithm (MOEA) in a code formulated in āCā language which is designed in a manner such that it is capable to handle the probabilistic constraints of a RBDO model.
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References
Aoues, Younes, and Alaa Chateauneuf. āBenchmark study of numerical methods for reliability-based design optimization.āĀ Structural and multidisciplinary optimizationĀ 41.2 (2010): 277ā294.
Calafiore, Giuseppe, and Fabrizio Dabbene, eds.Ā Probabilistic and randomized methods for design under uncertainty. Springer Science & Business Media, 2006.
Lee, Ikjin, K. K. Choi, and David Gorsich. āSystem reliability-based design optimization using the MPP-based dimension reduction method.āĀ Structural and Multidisciplinary OptimizationĀ 41.6 (2010): 823.
Youn, Byeng D., et al. āReliability-based design optimization for crashworthiness of vehicle side impact.āĀ Structural and Multidisciplinary OptimizationĀ 26.3 (2004): 272ā283
Jamali, Ali, A. Hajiloo, and Nader Nariman-Zadeh. āReliability-based robust Pareto design of linear state feedback controllers using a multi-objective uniform-diversity genetic algorithm (MUGA).ā Expert systems with Applications 37.1 (2010): 401ā413.
Kovach Jami, Cho Rae Byung, Antony Jiju, āDevelopment of an experiment-based robust design paradigm for multiple quality characteristics using physical programming,ā Int J Adv Manuf Technol, vol. 35, 2008, pp. 1100ā1112.
Brito, T. G., et al. āA normal boundary intersection approach to multiresponse robust optimization of the surface roughness in end milling process with combined arrays.ā Precision Engineering 38.3 (2014): 628ā638.
Deb, K. (1999); āMulti-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problemsā; Massachusetts Institute of Technology (MIT) Press: Evolutionary Computation, Vol 7, No.2 (pp: 205ā230).
Marler, R. Timothy, and Jasbir S. Arora. āSurvey of multi-objective optimization methods for engineering.āĀ Structural and multidisciplinary optimizationĀ 26.6 (2004): 369ā395.
Wu, J. et al (2001); āSafety-Factor Based Approach for Probability-Based Design Optimizationā; The American Institute of Aeronautics and Astronautics (AIAA), 1522.
Hasofer, Abraham M., and Niels C. Lind. āExact and invariant second-moment code format.ā Journal of the Engineering Mechanics division 100.1 (1974): 111ā121.
Nguyen, Tam H., Junho Song, and Glaucio H. Paulino. āSingle-loop system reliability-based design optimization using matrix-based system reliability method: theory and applications.āĀ Journal of Mechanical DesignĀ 132.1 (2010): 011005.
Chen, Xiaoguan, Timothy K. Hasselman, and Douglas J. Neill. āReliability based structural design optimization for practical applications.ā Proceedings of the 38th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference. 1997.
Liang, Jinghong, Zissimos P. Mourelatos, and Jian Tu. āA single-loop method for reliability-based design optimisation.āĀ International Journal of Product DevelopmentĀ 5.1ā2 (2008): 76-92.
Du, Xiaoping, and Wei Chen. āSequential optimization and reliability assessment method for efficient probabilistic design.ā Transactions-American Society of Mechanical Engineers Journal of Mechanical Design (2004): 225ā233.
Mourelatos, Zissimos P., and Jinghong Liang. āAn efficient unified approach for reliability and robustness in engineering design.ā NSF Workshop on Reliable Engineering Computing, Savannah, Georgia. 2004.
Shan, Songqing, and G. Gary Wang. āReliable design space and complete single-loop reliability-based design optimization.ā Reliability Engineering & System Safety 93.8 (2008): 1218ā1230.
Phadke, Madhav S. āQuality engineering using design of experiments.ā InĀ Quality control, robust design, and the Taguchi method, pp. 31ā50. Springer US, 1989.
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Jain, N., Badhotiya, G.K., Chauhan, A.S., Purohit, J.K. (2018). Reliability-Based Design Optimization Using Evolutionary Algorithm. In: Perez, G., Tiwari, S., Trivedi, M., Mishra, K. (eds) Ambient Communications and Computer Systems. Advances in Intelligent Systems and Computing, vol 696. Springer, Singapore. https://doi.org/10.1007/978-981-10-7386-1_34
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DOI: https://doi.org/10.1007/978-981-10-7386-1_34
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