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

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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.

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, University at Buffalo, 318 Jarvis Hall, Buffalo, NY, 14260-4400, USA

    Po-Wen Chiu & Christina L. Bloebaum

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  1. Po-Wen Chiu
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  2. Christina L. Bloebaum
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Correspondence to Christina L. Bloebaum.

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Chiu, PW., Bloebaum, C.L. Hyper-Radial Visualization (HRV) method with range-based preferences for multi-objective decision making. Struct Multidisc Optim 40, 97–115 (2010). https://doi.org/10.1007/s00158-009-0361-9

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  • DOI: https://doi.org/10.1007/s00158-009-0361-9

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