Genetic Algorithms for Composite Laminate Design and Optimization

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1997-02-05

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Virginia Tech

Abstract

Genetic algorithms are well known for being expensive optimization tools, especially if the cost for the analysis of each individual design is high. In the past few years, significant effort has been put forth in addressing the high computational cost GAs. The research conducted in the first part of this thesis continues this effort by implementing new multiple elitist and variable elitist selection schemes for the creation of successive populations in the genetic search process. The new selection schemes allow the GA to take advantage of a greater amount of important genetic information that may be contained in the parent designs, information that is not utilized when using a traditional elitist method selection scheme. By varying the amount of information that may be passed to successive generations from the parent population, the explorative and exploitative characteristics of the GA can be adjusted throughout the genetic search also. The new schemes provided slight reductions in the computational cost of the GA and produced many designs with good fitness' in the final population, while maintaining a high level of reliability. Genetic algorithms can be easily adapted to many different optimization problems also. This capability is demonstrated by modifying the basic GA, which utilizes a single chromosome string, to include a second string so that composite laminates comprised of multiple materials can be studied with greater efficiently. By using two strings, only minor adjustments to the basic GA were required. The modified GA was used to simultaneously minimize the cost and weight of a simply supported composite plate under different combinations of axial loading. Two materials were used, with one significantly stronger, but more expensive than the other. The optimization formulation was implemented by using convex combinations of cost and weight objective functions into a single value for laminate fitness, and thus required no additional modifications to the GA. To obtain a Pareto-optimal set of designs, the influence of cost and weight on the overall fitness of a laminate configuration was adjusted from one extreme to the other by adjusting the scale factors accordingly. The modified GA provided a simple yet reliable means of designing high performance composite laminates at costs lower than laminates comprised of one material.

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composite laminate, genetic algorithm, buckling, stacking sequence, Design, Optimization

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