Optimization of Composite Laminates for Fundamental Frequency

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To maximize the fundamental frequency of composite laminates, a hybrid optimization algorithm which combines the respective merits of the genetic algorithm and the simulated annealing algorithm is adopted. This hybrid algorithm also incorporates adaptive mechanisms designed to adjust the probabilities of the cross-over and mutation operators. Then, this algorithm is applied to optimize the fiber angle of each layer of a composite laminate such that its fundamental natural frequency is maximized. The results indicate that this hybrid optimization algorithm could quickly find the optimal fiber angles and maximize the fundamental frequency, even under complicated choices of fiber angle and boundary conditions.

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71-75

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May 2015

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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[1] Dj.M. Maric, P.F. Meier and S.K. Estreicher: Mater. Sci. Forum Vol. 83-87 (1992), p.119.

Google Scholar

[2] M.A. Green: High Efficiency Silicon Solar Cells (Trans Tech Publications, Switzerland 1987).

Google Scholar

[3] Y. Mishing, in: Diffusion Processes in Advanced Technological Materials, edtied by D. Gupta Noyes Publications/William Andrew Publising, Norwich, NY (2004), in press.

Google Scholar

[14] D. Brown, C. Huntley and A.A. Spillane,: A parallel genetic heuristics for the quadratic assignment problem. In Proceeding of Third International Conference on Neural Networks (1989).

Google Scholar

[25] K.C. Tan and Y. Li:, System identification and linearization using genetic algorithms with simulated annealing. In Proceeding IEEE Genetic Algorithms in Engineering System: Innovations and Applications 414 (1995).

DOI: 10.1049/cp:19951043

Google Scholar

[36] I.K. Jeong and J.J. Lee:, Adaptive simulated annealing genetic algorithm for system identification. Engineering Applications of Artificial Intelligence 9 (1996).

DOI: 10.1016/0952-1976(96)00049-8

Google Scholar

[47] S.F. Hwang and R.S. He:, A hybrid real-parameter genetic algorithm for function optimization. Advanced Engineering Informatics 20 (1006) 7-21.

DOI: 10.1016/j.aei.2005.09.001

Google Scholar

[5] S.F. Hwang and R.S. He:, Improving real-parameter genetic algorithm with simulated annealing for engineering problems. Advances in Engineering Software 37 (2006) 406–418. Information on http: /www. weld. labs. gov. cn.

DOI: 10.1016/j.advengsoft.2005.08.002

Google Scholar