Hyper-Radial Visualization (HRV) method with range-based preferences for multi-objective decision making

Abstract

A visualization-based methodology is developed in which a Hyperspace Pareto Frontier (HPF) can be represented for design concept selection. The new approach is termed the Hyper-Radial Visualization (HRV) method. The HRV method enables designers to investigate trade-off decisions between Pareto solutions by their relative position in an HRV-based visualization. Three a posteri range-based preference incorporation approaches are proposed in this paper that can be combined with HRV-based visualizations to enable designers to quickly identify better regions in high dimensional performance space for Multi-objective Optimization Problems (MOPs). The paper first explains the details of the HRV method, which can generate a meaningful representation of an HPF. Second, three color-coding preference schemes are proposed in this work to enable intuitive trade-off studies using the HRV-based HPF visualizations. Finally, several MOPs are used to investigate the performance of the HRV-based preference approaches that have been proposed. The viability and desirability of using the HRV for decision making support is also explored.