pymoo

SKILL.md

Pymoo - Multi-Objective Optimization in Python

Overview

Pymoo is a comprehensive Python framework for optimization with emphasis on multi-objective problems. Solve single and multi-objective optimization using state-of-the-art algorithms (NSGA-II/III, MOEA/D), benchmark problems (ZDT, DTLZ), customizable genetic operators, and multi-criteria decision making methods. Excels at finding trade-off solutions (Pareto fronts) for problems with conflicting objectives.

When to Use This Skill

This skill should be used when:

Solving optimization problems with one or multiple objectives

Finding Pareto-optimal solutions and analyzing trade-offs

Implementing evolutionary algorithms (GA, DE, PSO, NSGA-II/III)

Working with constrained optimization problems

Benchmarking algorithms on standard test problems (ZDT, DTLZ, WFG)

Customizing genetic operators (crossover, mutation, selection)

Visualizing high-dimensional optimization results

Making decisions from multiple competing solutions

Handling binary, discrete, continuous, or mixed-variable problems