The use of a genetic algorithm to improve the postbuckling strength of stiffened composite panels susceptible to secondary instabilities

2012 ◽  
Vol 94 (3) ◽  
pp. 883-895 ◽  
Author(s):  
B.G. Falzon ◽  
A. Faggiani
2014 ◽  
Vol 31 (1) ◽  
pp. 33-47
Author(s):  
Aleksandr Cherniaev

Purpose – The genetic algorithm (GA) technique is widely used for the optimization of stiffened composite panels. It is based on sequential execution of a number of specific operators, including the encoding of particular design variables. For instance, in the case of a stiffened composite panel, the design variables that need to be encoded are: the number of plies and their stacking sequences in the panel skin and stiffeners. This paper aims to present a novel, implicit, heuristic approach for encoding composite laminates and, through its use, demonstrates an improvement in the optimization process. Design/methodology/approach – The stiffened panel optimization has been formulated as a constrained discrete minimum-weight design problem. GAs, which use both new encoding schemes and those previously described in the literature, have been used to find near-optimal solutions to the formulated problem. The influence of the new encoding scheme on the searching capabilities of the GA has been investigated using comparative analysis of the optimization results. Findings – The new encoding scheme allows the definition of stacking sequences in composites using shorter symbolic representations as compared with standard encoding operators and, as a result of this, a reduction in the problem design space. According to numerical experiments performed in this work, this feature enables GA to obtain near-optimal designs using smaller population sizes than those required if standard encoding schemes are used. Originality/value – The approach to encoding laminates presented in this paper is based on the original heuristics. In the context of GA-based optimization of stiffened composite panels, the use of the new approach rather than the standard encoding technique can lead to a significant reduction in computational time employed.


1996 ◽  
Vol 58 (3) ◽  
pp. 543-555 ◽  
Author(s):  
S. Nagendra ◽  
D. Jestin ◽  
Z. Gürdal ◽  
R.T. Haftka ◽  
L.T. Watson

2020 ◽  
Author(s):  
Valerio Acanfora ◽  
Andrea Sellitto ◽  
Aniello Riccio ◽  
Francesco Di Caprio

Author(s):  
Feng Zhang ◽  
Mingying Wu ◽  
Xinting Hou ◽  
Cheng Han ◽  
Xinhe Wang ◽  
...  

2016 ◽  
Vol 87 ◽  
pp. 111-119 ◽  
Author(s):  
Zhenkun Lei ◽  
Ruixiang Bai ◽  
Wang Tao ◽  
Xiao Wei ◽  
Ruijiao Leng

2002 ◽  
Author(s):  
Hiroaki Tsutsui ◽  
Akio Kawamata ◽  
Junichi Kimoto ◽  
Tomio Sanda ◽  
Nobuo Takeda

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