Multidimensional design visualization in multiobjective optimization

Abstract Some of the first attempts at using visualization methods to aid decisions in design and optimization are found in [1]. More recent advances in computer visualization and Virtual Reality (VR) [2-4] are allowing designers and scientists to interact and manipulate vast amounts of data. Until these innovations, computers were solely relied on to interpret results and compute answers based on programs written. It is now possible to interact with these large datasets even while they are being used in running analyses [5-11]. Users have the ability to compress large amounts of data into a visual format, to investigate trends and relationships that could not be seen otherwise, and then make informed decisions regarding a product or process design. As our ability to generate more and more data for increasingly large engineering models improves, the need for means of managing that data becomes greater. Information management from a decision-making perspective involves being able to capture and then represent significant information to a designer so that he or she can make effective and efficient decisions. However, most visualization techniques used in engineering, such as graphs and charts, are limited to two-dimensional representations and at most three-dimensional representations. In this paper, we present a technique used to capture and represent engineering information in a multidimensional context. In this paper, we present an overview of the technique and provide details regarding two specialized functions for use in multidimensional and multiobjective optimization. This method is part of an effort to develop a comprehensive visualization-based optimization tool for multi-objective and robust design problems. Commercial companies such as Raytheon [12] and Boeing [3], among others, have attempted to improve their own design processes by taking advantage o