Pymoo: Multi-Objective Optimization in Python

Abstract

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

Keywords

Affiliated Institutions

• Michigan State University US

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