Getting StartedΒΆ

Learning a new framework, in general, can be rather challenging. Thus, this getting started guide aims to make the first steps with pymoo as simple as possible by demonstrating its capabilities on an example. This guide covers the essential steps when starting with multi-objective optimization and shall be helpful to solve your own optimization problems. Some basic understanding of optimization and knowledge of Python and NumPy are expected to follow.

This guide is structured as follows:


  • Preface: Basics and Challenges

  • Part I: A Constrained Bi-objective Optimization Problem

  • Part II: Find a Solution Set using Multi-objective Optimization

  • Part III: Multi-Criteria Decision Making

  • Part IV: Analysis of Convergence

  • Part V: Some more useful Information