Visualization of design spaces is a complex problem that has the potential to provide many benefits. Design spaces can be easily visualized with two or three design variables using a range of methods. However, once a problem exceeds this limit, direct visualization that captures all necessary behaviors becomes difficult. To visualize these higher dimensions, it is necessary to use visual cues such as color, size, and/or symbols to show the added dimensions. The disadvantage to using visual cues is the inability to expand much beyond three dimensions. This research focuses on using contextual self-organizing maps to provide a solution to visualizing high-dimensional design spaces by using the dimensionality-reduction capabilities of self-organizing maps. A visual representation is created by generating a self-organizing map and applying objective function values as the contextual labels. The map is then broken into three different maps containing separate contextual information, namely the mean, minimum, and standard deviations for each node. Qualitative information such as modality, curvature, and areas of interest can be determined from these maps. This information can be used to select and initialize a formal optimization algorithm. A number of unimodal and multimodal optimization functions were solved, after using contextual self-organizing maps to first map the design space, to test whether using contextual self-organizing maps to initialize an optimization algorithm improves performance. Initial points were selected from the generated maps and used in a pheromone particle swarm optimization method as well as a numerical method. Results were then analyzed for improvements and insights.
Visualizing Design Spaces Using Two-Dimensional Contextual Self-Organizing Maps
AIAA Journal ; 52 , 4 ; 725-738
2014-01-20
14 pages
Article (Journal)
Electronic Resource
English
Visualizing Design Spaces Using Two-Dimensional Contextual Self-Organizing Maps
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