Multiobjective Optimization - Visualization of Solution ...€¦ · Visualization of Solution Sets...

Multiobjective OptimizationVisualization of Solution Sets in Many-Objective Optimization

Facult:

Eduardo G. CarranoFrederico G. Guimarães

Lucas S. Batista

{egcarrano,fredericoguimaraes,lusoba}@ufmg.brwww.ppgee.ufmg.br/∼lusoba

Universidade Federal de Minas GeraisPrograma de Pós-Graduação em Engenharia Elétrica, Brasil

Visualization of Solution Sets References

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1 Visualization of Solution SetsPresentation

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Many-objective optimization problem

Multiobjective optimization problem in which m > 3.

minx

f(x) ∈ Rm, x ∈ F

f : X ⊂ Rn 7→ Y ⊂ Rm

F =

gi(x) ≤ 0; i = 1, . . . ,phj(x) = 0; j = 1, . . . ,qx ∈ X

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Popular Visualization Approaches for MaOPs

In a low-dimensional space with two or three objectives, a scatterplot shows the location, distribution, and shape of the approxima-ted front, where each axis directly represents one objective.

From a scatter plot, a decision-maker easily makes choices andpicks up preferred solutions.

However, it cannot be extended to high-dimensional objective spa-ces with more than 3 criteria.

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In order to illustrate the maps:

Consider the well-known test problem DTLZ2, and generated resultsfor m = 2, 3, 5 objectives.

Suppose the solutions have been coloured according to the objec-tive m on which the solution performs “best”.

In this way: 1 = red, 2 = green, 3 = blue, 4 = cyan, 5 = black.

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Scatter plot representation:

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Visualization Based on Parallel Coordinate System:

For an m-dimensional objective space, the respective parallel coor-dinate system would contain m parallel axes, each of which repre-sents one objective.

This method can directly show the original objective values of solu-tions.

In the objective space, if objectives of interest are positioned adja-cent to each other, this method can also provide information aboutthe tradeoff relationships between objectives and contour informa-tion of the approximate Pareto front for decision-maker.

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Parallel coordinates.

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Visualization Based on Parallel Coordinate System:Heatmap.

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Visualization Based on Mapping:

This type of methods uses some more sophisticated mapping tech-niques to perform dimension reduction to the 2-D space.

All mapping methods try to preserve Pareto dominance among indi-viduals and retain local distances between each pair of them.

They are often computationally expensive and not robust as themapping heavily depends on the values of the objective vectors inthe approximate front.

Meanwhile, all kinds of mapping methods cannot avoid losing someimportant information.

Moreover, in this 2-D map, it is still not easy to discover the shapeof the approximate front and tradeoffs between objectives.

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Visualization Based on Parallel Coordinate System:Pairwise coordinate plot.

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Visualization Based on Parallel Coordinate System:PCA map.As the colouring shows, the solutions are overlayed in such a waythat is difficult to distinguish among them; moreover, the informationfrom clusters are lost.

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Visualization Based on Mapping:Self-organizing map.Nearby vectors in the input high-dimensional space are mapped tonearby units (neurons) in SOMs.

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Generative Topographical Mapping (GTM).

It is an alternative to the SOM map. Individuals have been broadlyclustered into similar groups, but there is an imperfect separation.

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Neuroscale

Preserves the local distances, but segregation into distinct regionsis unclear.

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Sammon mapping (preserves the local distances).

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Visualization Based on Mapping:Radial coordinate visualization.Preserves well the distribution of vectors but it cannot distinguishthe shape of the approximate Pareto fronts.

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Visualization Based on Mapping:Isomap.Preserves the intrinsic geometry of solutions when mapping to 2-Dspace, where each solution is linked only to its closest neighbors.

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Conclusions

Some of the visualization techniques can lost information;

Each visualization technique has specific properties;

This way, each one can be useful depending on the criterion con-sidered;

The visualization of solution sets in MaO is an open problem!

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References

David J. Walker & Richard M. Everson & Jonathan E. Fieldsend, Visualising So-lution Sets in Many-objective Optimisation, IEEE TEC, v. 17, no. 2, p. 165–184,2013.

Zhenan He and Gary G. Yen, Visualization and Performance Metric in Many-Objective Optimization, IEEE TEC, v. 20, no. 3, p. 386–402, 2016.

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