Platypus-Opt 1.4.1

Multiobjective optimization in Python

Platypus

What is Platypus?

Platypus is a framework for evolutionary computing in Python with a focus on multiobjective evolutionary algorithms (MOEAs). It differs from existing optimization libraries, including PyGMO, Inspyred, DEAP, and Scipy, by providing optimization algorithms and analysis tools for multiobjective optimization. It currently supports NSGA-II, NSGA-III, MOEA/D, IBEA, Epsilon-MOEA, SPEA2, GDE3, OMOPSO, SMPSO, and Epsilon-NSGA-II. For more information, see our examples and online documentation.

Example

For example, optimizing a simple biobjective problem with a single real-valued decision variables is accomplished in Platypus with:

    from platypus import NSGAII, Problem, Real

    def schaffer(x):
        return [x[0]**2, (x[0]-2)**2]

    problem = Problem(1, 2)
    problem.types[:] = Real(-10, 10)
    problem.function = schaffer

    algorithm = NSGAII(problem)
    algorithm.run(10000)

Installation

To install the latest Platypus release, run the following command:

    pip install platypus-opt

To install the latest development version of Platypus, run the following commands:

    pip install -U build setuptools
    git clone https://github.com/Project-Platypus/Platypus.git
    cd Platypus
    python -m build
    python -m pip install --editable .

Anaconda

Platypus is also available via conda-forge.

    conda config --add channels conda-forge
    conda install platypus-opt

For more information, see the feedstock.

Citation

If you use this software in your work, please cite it as follows (APA style):

Hadka, D. (2024). Platypus: A Framework for Evolutionary Computing in Python (Version 1.3.0) [Computer software]. Retrieved from https://github.com/Project-Platypus/Platypus.

License

Platypus is released under the GNU General Public License.