Contextual Self-Organizing Map Visualization to Improve Optimization Solution Convergence

5is a type of artificial neural network that uses dimensionality reduction to allow for visualization of a high dimensional problem in a low dimensional space, while preserving the topology of the data itself. Kohnen’s SOMs, however, do not allow the map to categorize the data represented in each node. Contextual SOMs alleviate this problem by labeling individual nodes. This allows a user to quickly identify each node, providing an overall view of the design space.뀀ഀȠ Using CSOMs as a pre-optimization step allows a designer to select an initial starting point for an algorithm and to select an optimization method based on the modality and curvature of the data. By identifying nodes that may contain minimum values the optimization algorithm is passed starting points that may increase the solution accuracy, reliability while decreasing solution time. In this study multiple unimodal and multimodal optimization problems were solved using CSOMs as a pre-optimization step. Multi-modal problems were solved using a pheromone particle swarm optimization method (PSO) 6 while unimodal problems were solved using a QuasiNewton Line search implemented through Matlab 7 .뀀ഀȠ 뀀ഀȠ