ties598 nonlinear multiobjective...
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TIES598 Nonlinear Multiobjective Optimization
Visualization aspects in multiobjective optimization
spring 2017
Jussi Hakanen & Vesa [email protected]
Contents
Visualization as a field of science
Basic visualization for 2 & 3 objectives
Visualization techniques for more than 3 objectives
Existing visualizations used in MOO
Advanced technologies
Visualization research fieldsScientific visualization
– Visualization of three-dimensional phenomena (architectural, meteorological, medical, biological, etc.), emphasis being on realistic renderings of volumes, surfaces, illumination sources etc., perhaps with a dynamic (time) component
Information visualization– Information visualization is the use of computer-supported, interactive,
visual representations of abstract data to amplify cognition*
Visual analytics (http://www.visual-analytics.eu/) – Science of analytical reasoning facilitated by interactive visual
interfaces**– Visual analytics combines automated analysis techniques with
interactive visualizations for an effective understanding, reasoning and decision making on the basis of very large and complex data sets***
– https://www.youtube.com/watch?v=5i3xbitEVfs
* S. Card, J. Mackinlay, and B. Shneiderman. Readings in Information Visualization: Using Vision to Think.
Morgan Stanley Publishers, 1999
** J. J. Thomas & K. A. Cook, A visual analytics agenda, IEEE Computer Graphics and Applications, 26, 2006
*** D. Keim et al., Visual analytics: Definition, process, and challenges. In: Information Visualization: Human-
Centered Issues and Perspectives, 2008
Visualization in multiobjective optimization
Typically, solutions in the objective space are visualized– Objective space: performance of solutions– Decision space: implementation of solutions
Some examples of visualizations combining both spacesRecent survey– K. Miettinen, Survey of methods to visualize alternatives in
multiple criteria decision making problems, OR Spectrum, 36, 2014
Visualization for more than 3 objectives: indirect visualization has to be used
Visualizations for 2D/3D
Direct visualization of
– Pareto front
– Individual (nondominated solutions)
Scatter plot
Dimensionality reduction
Dimensionality of the objective space can also be reduced to enable visualization, e.g.
– Principal component analysis (PCA)
– Self organizing maps (SOM): nonlinear generalization of PCA
– Multidimensional scaling (MDS): maps the solutions on a plane while trying to preserve distances between them
Visualizations for more than threeobjectives
Indirect visualization
Typically a small set of individual solutions are visualized
Parallel coordinate plots
• Web-based PCP tool: https://reed.cee.cornell.edu/parallel-axis-categories/parallel/
• Interesting blog:
https://waterprogramming.wordpress.com/?s=visualization&submit=Search
Other visualization techniques…
Existing visualizations used in MOO
knowCube– H. L. Trinkaus & T. Hanne, knowCube: A visual and interactive support for
multicriteria decision making, Computers and Operations Research, 32, 2005
Existing visualizations used in MOO
Interactive decision maps– A. V. Lotov et al., Interactive Decision Maps: Approximation and
Visualization of Pareto Frontier, Kluwer Academic Publishers, 2004
Existing visualizations used in MOO
Heat map visualization– J. Hettenhausen et al., Interactive multi-objective particle swarm optimization with
heatmap-visualization-based user interface, Engineering Optimization, 42, 2010
Existing visualizations used in MOO
RadVis: maps M-dimensional points to 2D space using nonlinear mapping
3D radial coordinate visualization (3D RadVis)– A. Ibrahim et al., 3D-RadVis: Visualization of Pareto front in many-objective optimization, In: 2016 IEEE CEC
conference, 2016
3D RadVis: third dimension for the shape and convergence of the solution set (distance from a reference hyperplane)
More illustrations in: http://ieeexplore.ieee.org/document/7743865/
A demo
IND-NIMBUS
Optimus
Recommendations for user interfaces
In the visualization fields
Use linked visualizations where different types of visualizations are connected such that a solution highlighted in any visualization becomes also highlighted in other visualizations
Utilize interactive visualization techniques where the user can manipulate them making the system feel responsive
How this could look like…?
f1
f4
f3
f2
f5
f1 f2 f3 f4 f5
1 15.979 417.52 22.817 15026.0 9655.8
2 16.111 426.55 1.521 14444.0 8947.0
3 16.466 421.95 20.998 14976.0 9729.6
4 16.851 415.46 22.75 15003.0 9721.0
5 17.404 416.8 17.513 14971.0 9762.7
Solution 2
Solutions after 1st interaction
f1 f2 f3 f4 f5
1 15.979 417.52 22.817 15026.0 9655.8
2 16.111 426.55 1.521 14444.0 8947.0
3 16.466 421.95 20.998 14976.0 9729.6
4 16.851 415.46 22.75 15003.0 9721.0
5 17.404 416.8 17.513 14971.0 9762.7
6 16.007 425.15 8.0389 14591.0 9132.4
7 16.077 425.16 10.999 14698.0 9265.4
best worstSolution 7
Solutions after 2nd interaction
Virtual reality vs. augmented reality
What is the difference?
– https://www.youtube.com/watch?v=aSjUxM5f-eM&feature=youtu.be&t=27
– https://www.youtube.com/watch?v=Eh24LpidJug
A look to the future…
https://www.youtube.com/watch?v=vg0A9Ve7SxE
Links
Firefighters: https://www.youtube.com/watch?v=5tfnmhl-A54Rolls Royce shore control: https://www.youtube.com/watch?v=vg0A9Ve7SxEElevator maintenance training: https://www.youtube.com/watch?v=8OWhGiyR4NsPlant maintenance: https://www.youtube.com/watch?v=QTuKcm8s4QQNASA: https://www.youtube.com/watch?v=IcJ-JuA_K7U&t=1m21s
VR vs. AR: https://www.youtube.com/watch?v=Eh24LpidJug, https://www.youtube.com/watch?v=aSjUxM5f-eMMixed reality: https://www.youtube.com/watch?v=Ic_M6WoRZ7kHolograph: https://www.youtube.com/watch?v=vOKVofs5rEgVisual analytics: https://www.youtube.com/watch?v=5i3xbitEVfs
Material
K. Miettinen, Survey of methods to visualize alternatives in multiple criteria decision making problems, OR Spectrum, 36:3-37, 2014T. Tusar & B. Filipic, Visualization of Pareto Front Approximations in Evolutionary Multiobjective Optimization: A Critical Review and the Prosection Method, IEEE Transactions on Evolutionary Computation, 19:225-245 , 2015A. Ibrahim et al., 3D-RadVis: Visualization of Pareto front in many-objective optimization, In: 2016 IEEE CEC, IEEE, 736-745 , 2016Z. He & G. Yen, Visualization and Performance Metric in ManyObjective Optimization, IEEE Transactions on Evolutionary Computation, 20:386-402, 2016J. Kehrer & H. Hauser, Visualization and visual analysis of multifaceted scientific data: A survey, IEEE Transactions on Visualization and Computer Graphics, 19(3):495–512, 2013S. Liu et al., A survey on information visualization: recent advances and challenges, The Visual Computer, 30(12):1373–1393, 2014S. Tarkkanen et al., Incremental user-interface development for interactive multiobjective optimization, Expert Systems with Applications, 40:3220–3232, 2013J. J. Thomas & K. A. Cook, A visual analytics agenda, IEEE Computer Graphics and Applications, 26(1):10–13, 2006