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Open source library for for interactive multiobjective optimization

Project description

# DESDEO README #

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

[Documentation is available.](https://desdeo.readthedocs.io/en/latest/)

[Background and publications available on the University of Jyväskylä Research Group in Industrial Optimization web pages.](https://desdeo.it.jyu.fi)

## Introduction ##

There exist many methods to solve [multiobjective optimization](https://en.wikipedia.org/wiki/Multi-objective_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](https://en.wikipedia.org/wiki/Multi-objective_optimization#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.

## Architecture ##

Overview of the current DESDEO architecture is shown in diagram

![DESDEO Overview](https://github.com/industrial-optimization-group/DESDEO/raw/master/docs/design/overview.png)

## Interactive Methods ##

### NAUTILUS Method ###

Most interactive methods developed for solving multiobjective
optimization problems sequentially generate Pareto optimal solutions
and the decision maker must always trade-off to get a new
solution. Instead, the family of interactive trade-off-free methods
called NAUTILUS starts from the worst possible objective values and
improves every objective function step by step according to the
preferences of the decision maker. Recently, the NAUTILUS family has
been presented as a general NAUTILUS framework consisting of several
modules. To extend the applicability of interactive methods, it is
recommended that a reliable software implementation, which can be
easily connected to different simulators or modelling tools, is
publicly available. In this paper, we bridge the gap between
presenting an algorithm and implementing it and propose a general
software framework for the NAUTILUS family which facilitates the
implementation of all the NAUTILUS methods, and even other interactive
methods. This software framework has been designed following an
object-oriented architecture and consists of several software blocks
designed to cover independently the different requirements of the
NAUTILUS framework. To enhance wide applicability, the implementation
is available as open source code.

## Examples ##

The functioning and flexibility of the DESDEO framework is
demonstrated with two numerical example problems.

## Development ##

### Set-up ###

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](https://pre-commit.com/#install).

### Tests ###

Tests use pytest. After installing pytest you can run:

pytest tests



Documentation
-------------

The full documentation is located at https://desdeo.readthedocs.io/en/latest/

Information about the academic project, including publications is available at http://desdeo.it.jyu.fi

History
=======

0.1.0 (2018-04-25)
-----------------

* First release

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