pymoo
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, SPEA2), 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. Targets stable pymoo 0.6.2, reviewed 2026-10-01 against current official docs and native toy runs.
Installation
uv pip install "pymoo==0.6.2"
For reproducible environments, pin a version: uv pip install "pymoo==0.6.2".
Dependencies: The released 0.6.2 wheel requires NumPy, SciPy, moocore, autograd, cma, matplotlib, alive_progress, and Deprecated. NumPy 2.x is supported. The current installation prose describes some dependencies as optional; the released package metadata governs installation. Joblib, Optuna and dill are separate dependencies for the corresponding recipes.
Documentation: https://pymoo.org/ — LLM-friendly index: https://pymoo.org/llms.txt