MOO Viz is currently under construction. Stay tuned.
Want to contribute? Check out the project on GitHub.
First time using MOO Viz? Try selecting one of the pre-loaded datasets using the dropdown menu.
Issues getting your data file to work? Checkout the table snippet below for how your columns should be setup.
Namely, the first column header should be "SolutionIndex" and contain integers that identify a solution within its frontier.
The second column should have the header "Frontier" and should identify which frontier the solution belongs to.
All solutions within a frontier should have the same value in the "Frontier" column.
(If you only have one frontier, that's perfectly fine.)
Additional columns are assumed to be solution data, with each column representing an objective.
Data should be in CSV format, and no string fields (including column headers) should include underscores ("_").
MOO Viz exists to help visualize and understand Pareto-optimal point sets, specifically, those produced by the solutions of multi-objective math programming models.
To compare these point sets (frontiers) MOO Viz can produce a handful of visualization aids, from simple tables to scatterplots to parallel coordinate plots.
MOO Viz also computes metrics that describe the competition between frontiers in a dataset and between objectives within a frontier.
The metrics include correlation coefficients, the hypervolume indicator and other measures commonly used in evolutionary multi-objective optimization.
Choose one of our pre-loaded datasets to see MOO Viz in action.
Set objectives' senses
For each of the following detected objectives, please select a sense (max/min):