Evolutionary multi-objective optimization of hybrid laminates
Abstract
The aim of the paper is to prepare an efficient method of the optimization of the hybrid fibre-reinforced laminates. Since the several optimization criteria which cannot be satisfied simultaneously are proposed, the multi-objective optimization methods have been employed. Different optimization criteria connected with the laminates' cost, the modal properties and the stiffness are considered. The multi-objective evolutionary algorithm which uses the Pareto approach has been used as the optimization method. To solve the boundary-value problem the finite element method commercial software has been employed. Numerical examples presenting the effectiveness of the proposed method are attached.
Keywords
multi-objective optimization, evolutionary algorithm, multi-layered laminate, modal analysis,References
[1] S. Adali, A. Richte, V.E. Verijenko, E.B. Summers. Optimal design of symmetric hybrid laminates with discrete ply angles for maximum buckling load and minimum cost. Composite Structures, 32: 409-415, 1995.[2] S. Adali, V.E. Verijenko. Optimum stacking sequence design of symmetric hybrid laminates undergoing free vibrations. Composite Structures, 54: 131-138, 200l.
[3] J.F. Aguilar Madeiraa, H. Rodriguesa, H. Pinaa. Multi-objective optimization of structures topology by genetic algorithms. Advances in Engineering Software, 36: 21-28, 2005.
[4] J. Arabas. Lectures on Evolutionary Algorithms (in Polish). Wydawnictwa Naukowo-Techniczne, 2001.
[5] W. Beluch. Evolutionary identification and optimization of composite structures. In: III European Conference on Computational Mechanics, ECCM 2006, Lisbon, CD-ROM, 2006.
Published
Aug 17, 2022
How to Cite
BELUCH, Witold; BURCZYŃSKI, Tadeusz; DŁUGOSZ, Adam.
Evolutionary multi-objective optimization of hybrid laminates.
Computer Assisted Methods in Engineering and Science, [S.l.], v. 14, n. 4, p. 569-578, aug. 2022.
ISSN 2956-5839.
Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/790>. Date accessed: 23 dec. 2024.
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