Metadata-Version: 1.1
Name: desdeo
Version: 0.1.1
Summary: Open source library for for interactive multiobjective optimization
Home-page: https://github.com/industrial-optimization-group/DESDEO
Author: Vesa Ojalehto
Author-email: vesa.ojalehto@gmail.com
License: MPL 2.0
Description-Content-Type: text/markdown
Description: # DESDEO README #
        
        <p align="center">
        <a href="https://desdeo.readthedocs.io/en/latest/?badge=latest"><img alt="Documentation Status" src="https://readthedocs.org/projects/desdeo/badge/?version=latest"></a>
        <a href="https://travis-ci.com/industrial-optimization-group/DESDEO"><img alt="Build Status" src="https://travis-ci.com/industrial-optimization-group/DESDEO.svg?branch=master"></a>
        <a href="https://github.com/ambv/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
        </p>
        
        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
        
        ### Release process ###
        
        1. Make a release commit in which the version is incremented in setup.py and an entry added to HISTORY.md
        
        2. Make a git tag of this commit with `git tag v$VERSION`
        
        3. Upload to PyPI with `python setup.py sdist bdist_wheel` and `twine upload dist/*`
        
        
        
        # 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 #
        
        ## 0.1.1 (2018-05-21) ##
        
        * Convert package description to use Markdown
        
        ## 0.1.0 (2018-04-25) ##
        
        * First release
        
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: PyPy
