Title page for ETD etd-08122005-135419

Type of Document Dissertation
Author Adams, David Bruce
Author's Email Address daadams3@vt.edu
URN etd-08122005-135419
Title Optimization Frameworks for Discrete Composite Laminate Stacking Sequences
Degree PhD
Department Computer Science
Advisory Committee
Advisor Name Title
Watson, Layne T. Committee Chair
Anderson-Cook, Christine M. Committee Member
Gürdal, Zafer Committee Member
Heath, Lenwood S. Committee Member
Ribbens, Calvin J. Committee Member
  • Blending
  • Decomposition
  • Genetic Algorithms
  • Composite Laminates
  • Combinatorial Optimization
  • Parallel Computing
Date of Defense 2005-07-20
Availability unrestricted
Composite panel structure optimization is commonly decomposed into panel optimization

subproblems, with speci ed local loads, resulting in manufacturing incompatibilities

between adjacent panel designs. Using genetic algorithms to optimize local panel stacking

sequences allows panel populations of stacking sequences to evolve in parallel and send

migrants to adjacent panels, so as to blend the local panel designs globally. The blending

process is accomplished using the edit distance between individuals of a population and

the set of migrants from adjacent panels. The objective function evaluating the tness of

designs is modi ed according to the severity of mismatches detected between neighboring

populations. This lays the ground work for natural evolution to a blended global solution

without leaving the paradigm of genetic algorithms. An additional method applied here

for constructing globally blended panel designs uses a parallel decomposition antithetical

to that of earlier work. Rather than performing concurrent panel genetic optimizations, a

single genetic optimization is conducted for the entire structure with the parallelism solely

within the tness evaluations. A guide based genetic algorithm approach is introduced to

exclusively generate and evaluate valid globally blended designs, utilizing a simple masterslave

parallel implementation, implicitly reducing the size of the problem design space and

increasing the quality of discovered local optima.

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