Research Area: | Research Publication | Year: | 2002 | ||
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Type of Publication: | Technical Report | ||||
Authors: | Eddy, John; Lewis, Kemper E. | ||||
Abstract: | The optimization of many realistic large-scale
engineering systems can be computationally
expensive. The evaluation of a single design
configuration can take minutes or hours, and although
computing power is steadily increasing, the
complexity of the analysis codes continues to keep
pace. In this paper a novel hybrid optimization
method is introduced to efficiently find the global
optimal of complex, highly multimodal systems. The
motivation lies in the fact that to optimize many
realistic engineering systems often requires numerous
computationally expensive analyses to be performed.
Heuristic optimization algorithms such as Simulated
Annealing or Genetic Algorithms often can locate near
optimal solutions but can require many function
evaluations. Local search algorithms, including both
gradient and non-gradient based methods, are quite
efficient at finding the optimal within convex areas of
the design space but often fail to find the global
optimal in multimodal design spaces. The hybrid
optimization approach presented in this work switches
between global and local search methods based on the
local topography of the design space. The global and
local optimizers work in concert to efficiently locate
quality design points better that either could alone. To
demonstrate the usefulness of the approach presented
in this paper, two case studies of differing complexity
are considered. |
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Comments: | 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization
4-6 September 2002, Atlanta, Georgia |
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Full text: AIAA.2002.5429.pdf
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