Details

Autor(en) / Beteiligte

• Blank, Julian
• Deb, Kalyanmoy

Titel

Pymoo: Multi-Objective Optimization in Python

Ist Teil von

• IEEE access, 2020, Vol.8, p.89497-89509

Ort / Verlag

Piscataway: IEEE

Erscheinungsjahr

2020

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Python has become the programming language of choice for research and industry projects related to data science, machine learning, and deep learning. Since optimization is an inherent part of these research fields, more optimization related frameworks have arisen in the past few years. Only a few of them support optimization of multiple conflicting objectives at a time, but do not provide comprehensive tools for a complete multi-objective optimization task. To address this issue, we have developed pymoo , a multi-objective optimization framework in Python. We provide a guide to getting started with our framework by demonstrating the implementation of an exemplary constrained multi-objective optimization scenario. Moreover, we give a high-level overview of the architecture of pymoo to show its capabilities followed by an explanation of each module and its corresponding sub-modules. The implementations in our framework are customizable and algorithms can be modified/extended by supplying custom operators. Moreover, a variety of single, multi- and many-objective test problems are provided and gradients can be retrieved by automatic differentiation out of the box. Also, pymoo addresses practical needs, such as the parallelization of function evaluations, methods to visualize low and high-dimensional spaces, and tools for multi-criteria decision making. For more information about pymoo , readers are encouraged to visit: https://pymoo.org .