Visualizing Design Spaces Using Two-Dimensional Contextual Self-Organizing Maps

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, a...