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Open source library for for interactive multiobjective optimization

Project description


Available on PyPI Documentation Status Build Status Code style: black

DESDEO is a free and open source Python-based framework for developing and experimenting with interactive multiobjective optimization.

Documentation is available.

Background and publications available on the University of Jyväskylä Research Group in Industrial Optimization web pages.

Try in your browser

You can try a guided example problem in your browser: choose how to deal with river pollution using NIMBUS. You can also browse the other examples.

What is interactive multiobjective optimization?

There exist many methods to solve multiobjective optimization problems. Methods which introduce some preference information into the solution process are commonly known as multiple criteria decision making methods. When using so called interactive methods, the decision maker (DM) takes an active part in an iterative solution process by expressing preference information at several iterations. According to the given preferences, the solution process is updated at each iteration and one or several new solutions are generated. This iterative process continues until the DM is sufficiently satisfied with one of the solutions found.

Many interactive methods have been proposed and they differ from each other e.g. in the way preferences are expressed and how the preferences are utilized when new solutions. The aim of the DESDEO is to implement aspects common for different interactive methods, as well as provide framework for developing and implementing new methods.


From conda-forge using Conda

This is the recommended installation method, especially for those who are newer to Python. First download and install the Anaconda Python distribution.

Next, run the following commands in a terminal:

conda config --add channels conda-forge
conda install desdeo desdeo-vis

Note: if you prefer not to install the full Anaconda distribution, you can install miniconda instead.

From PyPI using pip

Assuming you have Pip and Python 3 installed, you can install desdeo from PyPI by running the following command in a terminal:

pip install desdeo[vis]

This installs desdeo and desdeo-vis, which you will also want in most cases.

Getting started with example problems

To proceed with this section, you must first install Jupyter notebook. If you're using Anaconda, you already have it!

You can copy the example notebooks to the current directory by running:

python -m desdeo_notebooks

You can then open them using Jupyter notebook by running:

jupyter notebook

After trying out the examples, the next step is to read the full documentation.



You should install the git pre-commit hook so that code formatting is kept consistent automatically. This is configured using the pre-commit utility. See the installation instructions.

If you are using pipenv for development, you can install desdeo and its dependencies after obtaining a git checkout like so:

pipenv install -e .[docs,dev,vis]


Tests use pytest. After installing pytest you can run:

pytest tests

Release process

  1. Make a release commit in which the version is incremented in and an entry added to

  2. Make a git tag of this commit with git tag v$VERSION

  3. Push -- including the tags with git push --tags

  4. Upload to PyPI with python sdist bdist_wheel and twine upload dist/*


The full documentation is located at

Information about the academic project, including publications is available at


0.2.0 (2018-08-28)

  • Documentation improvements

0.1.5 (2018-08-27)

  • Specify prompt-toolkit as <2 to avoid Jupyter-console incompatibility
  • Documentation improvements

0.1.4 (2018-07-16)

  • Add CylinderProblem
  • Add background documentation
  • Add desdeo-vis as extra dependency

0.1.3 (2018-06-18)

  • Fix PyPI package

0.1.2 (2018-06-13)

  • Improvements to automatically generated documentation
  • Move river pollution example into desdeo.problem.toy module
  • Allow solutions as well as objective functions to be obtained by adding ResultSet class
  • Improvements to NIMBUS
    • Add missing NIMBUS scalarising functions
    • Add method to generate in-between solutions for NIMBUS
  • Add RangeEstimator module for finding the nadir/ideal with a payoff table

0.1.1 (2018-05-21)

  • Convert package description to use Markdown

0.1.0 (2018-04-25)

  • First release

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