Title page for ETD etd-4246173049751491

Type of Document Master's Thesis
Author Soremekun, Grant A. E.
URN etd-4246173049751491
Title Genetic Algorithms for Composite Laminate Design and Optimization
Degree Master of Science
Department Engineering Science and Mechanics
Advisory Committee
Advisor Name Title
Thangjitham, Surot
Watson, Layne T.
Gürdal, Zafer Committee Chair
  • composite laminate
  • genetic algorithm
  • buckling
  • stacking sequence
  • design
  • optimization
Date of Defense 1997-02-05
Availability unrestricted
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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