Abstract:Abstract. In Design by Shopping, designers explore the design space to gain an insight into trades and feasible and impractical solutions, as well as to learn about alternatives before optimization and selection. The design space consists of multidimensional sets of data and, in order to select the best design from amongst numerous alternatives, designers may use several different graphs. In this study, we test to find the most appropriate graph to indicate the best solution corresponding to a set of objective… Show more
“…Introduced by Inselberg (1985) and extensively described by Inselberg (1997Inselberg ( , 2009, parallel coordinates are similar to radar charts, except the dimensions are displayed as vertical side-byside axes instead of radially. This allows the method to scale well to many dimensions, and facilitates the comparison of values and identification of tradeoffs, trends and clusters in the data (Shenfield et al, 2007;Akle et al, 2017). Data points are depicted as polygonal lines (or polylines), which intersect the axes at their corresponding values.…”
Section: Interfaces For Multiobjective Interactive Optimizationmentioning
confidence: 99%
“…The main drawbacks of parallel coordinates include cluttering of the chart when displaying many alternatives, and the impossibility to visualize all pairwise relationships between dimensions in a single chart (Heinrich and Weiskopf, 2013;Johansson and Forsell, 2016). Studies also emphasize the need for users to receive basic training to better harness parallel coordinates (Shneiderman, 1996;Wolf et al, 2009;Johansson and Forsell, 2016;Akle et al, 2017). The recent developments of interactive data visualizations have greatly alleviated these limitations by allowing the user to filter the displayed solutions, reorder axes by dragging them to explore specific pairwise relationships, or change the visual aspect of lines such as color or opacity to reveal patterns across all dimensions (Bostock et al, 2011;Fieldsend, 2016).…”
Section: Interfaces For Multiobjective Interactive Optimizationmentioning
confidence: 99%
“…Several studies investigated the practical applicability of parallel coordinates in the context of multiobjective optimization. Akle et al (2017) studied their effectiveness in comparison to radar charts and combined tables for the balancing of multiple criteria and selection of preferred solutions. They found parallel coordinates to be the most effective and engaging approach for exploration, requiring less cognitive load and stress than the other charts.…”
Section: Interfaces For Multiobjective Interactive Optimizationmentioning
confidence: 99%
“…Given the widespread attention received by parallel coordinates, their adoption in the context of multiobjective optimization is not surprising. However, their use remains predominantly confined to a posteriori exploration of precalculated solutions (Bagajewicz and Cabrera, 2003;Xiao et al, 2007;Kipouros et al, 2008;Franken, 2009;Raphael, 2011;Rosenberg, 2012;Miettinen, 2014;Ashour and Kolarevic, 2015;Fieldsend, 2016;Akle et al, 2017;Bandaru et al, 2017a). Only a few studies suggested using parallel coordinates to steer the optimization procedures, but all adopt meta-heuristic approaches, limiting their applicability to smaller problems with few variables (Fleming et al, 2005;Stump et al, 2009;Sato et al, 2015;Hernández Gómez et al, 2016).…”
Section: Interfaces For Multiobjective Interactive Optimizationmentioning
confidence: 99%
“…Among these, parallel coordinates increasingly stand out among the most efficient approaches. They are known for their intuitive representations (Packham et al, 2005;Akle et al, 2017), as well as for occupying the least amount of space per criterion, making them highly scalable to many criteria (Fleming et al, 2005). Despite these strengths, like other visualizations, parallel coordinates also suffer from a lack of readability when displaying many solutions, and the difficulty to view all pairwise relationships in a single chart.…”
Interactive optimization methods are particularly suited for letting human decision makers learn about a problem, while a computer learns about their preferences to generate relevant solutions. For interactive optimization methods to be adopted in practice, computational frameworks are required, which can handle and visualize many objectives simultaneously, provide optimal solutions quickly and representatively, all while remaining simple and intuitive to use and understand by practitioners. Addressing these issues, this work introduces SAGESSE (Systematic Analysis, Generation, Exploration, Steering and Synthesis Experience), a decision support methodology, which relies on interactive multiobjective optimization. Its innovative aspects reside in the combination of (i) parallel coordinates as a means to simultaneously explore and steer the underlying alternative generation process, (ii) a Sobol sequence to efficiently sample the points to explore in the objective space, and (iii) on-the-fly application of multiattribute decision analysis, cluster analysis and other data visualization techniques linked to the parallel coordinates. An illustrative example demonstrates the applicability of the methodology to a large, complex urban planning problem.
“…Introduced by Inselberg (1985) and extensively described by Inselberg (1997Inselberg ( , 2009, parallel coordinates are similar to radar charts, except the dimensions are displayed as vertical side-byside axes instead of radially. This allows the method to scale well to many dimensions, and facilitates the comparison of values and identification of tradeoffs, trends and clusters in the data (Shenfield et al, 2007;Akle et al, 2017). Data points are depicted as polygonal lines (or polylines), which intersect the axes at their corresponding values.…”
Section: Interfaces For Multiobjective Interactive Optimizationmentioning
confidence: 99%
“…The main drawbacks of parallel coordinates include cluttering of the chart when displaying many alternatives, and the impossibility to visualize all pairwise relationships between dimensions in a single chart (Heinrich and Weiskopf, 2013;Johansson and Forsell, 2016). Studies also emphasize the need for users to receive basic training to better harness parallel coordinates (Shneiderman, 1996;Wolf et al, 2009;Johansson and Forsell, 2016;Akle et al, 2017). The recent developments of interactive data visualizations have greatly alleviated these limitations by allowing the user to filter the displayed solutions, reorder axes by dragging them to explore specific pairwise relationships, or change the visual aspect of lines such as color or opacity to reveal patterns across all dimensions (Bostock et al, 2011;Fieldsend, 2016).…”
Section: Interfaces For Multiobjective Interactive Optimizationmentioning
confidence: 99%
“…Several studies investigated the practical applicability of parallel coordinates in the context of multiobjective optimization. Akle et al (2017) studied their effectiveness in comparison to radar charts and combined tables for the balancing of multiple criteria and selection of preferred solutions. They found parallel coordinates to be the most effective and engaging approach for exploration, requiring less cognitive load and stress than the other charts.…”
Section: Interfaces For Multiobjective Interactive Optimizationmentioning
confidence: 99%
“…Given the widespread attention received by parallel coordinates, their adoption in the context of multiobjective optimization is not surprising. However, their use remains predominantly confined to a posteriori exploration of precalculated solutions (Bagajewicz and Cabrera, 2003;Xiao et al, 2007;Kipouros et al, 2008;Franken, 2009;Raphael, 2011;Rosenberg, 2012;Miettinen, 2014;Ashour and Kolarevic, 2015;Fieldsend, 2016;Akle et al, 2017;Bandaru et al, 2017a). Only a few studies suggested using parallel coordinates to steer the optimization procedures, but all adopt meta-heuristic approaches, limiting their applicability to smaller problems with few variables (Fleming et al, 2005;Stump et al, 2009;Sato et al, 2015;Hernández Gómez et al, 2016).…”
Section: Interfaces For Multiobjective Interactive Optimizationmentioning
confidence: 99%
“…Among these, parallel coordinates increasingly stand out among the most efficient approaches. They are known for their intuitive representations (Packham et al, 2005;Akle et al, 2017), as well as for occupying the least amount of space per criterion, making them highly scalable to many criteria (Fleming et al, 2005). Despite these strengths, like other visualizations, parallel coordinates also suffer from a lack of readability when displaying many solutions, and the difficulty to view all pairwise relationships in a single chart.…”
Interactive optimization methods are particularly suited for letting human decision makers learn about a problem, while a computer learns about their preferences to generate relevant solutions. For interactive optimization methods to be adopted in practice, computational frameworks are required, which can handle and visualize many objectives simultaneously, provide optimal solutions quickly and representatively, all while remaining simple and intuitive to use and understand by practitioners. Addressing these issues, this work introduces SAGESSE (Systematic Analysis, Generation, Exploration, Steering and Synthesis Experience), a decision support methodology, which relies on interactive multiobjective optimization. Its innovative aspects reside in the combination of (i) parallel coordinates as a means to simultaneously explore and steer the underlying alternative generation process, (ii) a Sobol sequence to efficiently sample the points to explore in the objective space, and (iii) on-the-fly application of multiattribute decision analysis, cluster analysis and other data visualization techniques linked to the parallel coordinates. An illustrative example demonstrates the applicability of the methodology to a large, complex urban planning problem.
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