Title page for ETD etd-08282002-112646


Type of Document Master's Thesis
Author Adams, David Bruce
Author's Email Address daadams3@vt.edu
URN etd-08282002-112646
Title Blending Methods for Composite Laminate Optimization
Degree Master of Science
Department Computer Science
Advisory Committee
Advisor Name Title
Watson, Layne T. Committee Chair
Allison, Donald C. S. Committee Member
Gürdal, Zafer Committee Member
Keywords
  • Parallel Computing
  • Combinatorial Optimization
  • Genetic Algorithms
  • Blending
  • Decomposition
  • Composite Laminates
Date of Defense 2002-08-16
Availability unrestricted
Abstract

Composite panel structure optimization is commonly decomposed into panel optimization subproblems, with specified 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 fitness of designs is modified 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 proposed 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 fitness evaluations. A guide based genetic algorithm approach is introduced to exclusively generate and evaluate valid globally blended designs, utilizing a simple master-slave parallel implementation, implicitly reducing the size of the problem design space and increasing the quality of discovered local optima.

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