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
Keywords	Blending Decomposition Genetic Algorithms Composite Laminates Combinatorial Optimization Parallel Computing
Date of Defense	2005-07-20
Availability	unrestricted
Abstract 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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