PAVED: Pareto Front Visualization for Engineering Design

Design problems in engineering typically involve a large solution space and several potentially conflicting criteria. Selecting a compromise solution is often supported by optimization algorithms that compute hundreds of Pareto‐optimal solutions, thus informing a decision by the engineer. However, t...

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Bibliographic Details
Published in:Computer graphics forum Vol. 39; no. 3; pp. 405 - 416
Main Authors: Cibulski, Lena, Mitterhofer, Hubert, May, Thorsten, Kohlhammer, Jörn
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.06.2020
Subjects:
Algorithms
CCS Concepts
Criteria
Design engineering
Design optimization
Domains
Electric motors
Engineers
Human‐centered computing → Visual analytics; Applied computing → Engineering
Iterative methods
Solution space
Visualization
Workflow
ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Summary:Design problems in engineering typically involve a large solution space and several potentially conflicting criteria. Selecting a compromise solution is often supported by optimization algorithms that compute hundreds of Pareto‐optimal solutions, thus informing a decision by the engineer. However, the complexity of evaluating and comparing alternatives increases with the number of criteria that need to be considered at the same time. We present a design study on Pareto front visualization to support engineers in applying their expertise and subjective preferences for selection of the most‐preferred solution. We provide a characterization of data and tasks from the parametric design of electric motors. The requirements identified were the basis for our development of PAVED, an interactive parallel coordinates visualization for exploration of multi‐criteria alternatives. We reflect on our user‐centered design process that included iterative refinement with real data in close collaboration with a domain expert as well as a summative evaluation in the field. The results suggest a high usability of our visualization as part of a real‐world engineering design workflow. Our lessons learned can serve as guidance to future visualization developers targeting multi‐criteria optimization problems in engineering design or alternative domains.
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.13990