Pymoo

If you're tackling optimization problems with conflicting objectives, this is the go-to Python framework. It's built around evolutionary algorithms like NSGA-II and NSGA-III that find Pareto-optimal trade-offs instead of single answers. The unified minimize() interface works across problem types, from simple single-objective GAs to many-objective problems with 5+ competing goals. Includes benchmark suites (ZDT, DTLZ) for validation and supports constraints, mixed variable types, and parallel evaluation. The ElementwiseProblem base class makes custom problems straightforward to define. Solid for engineering design, resource allocation, or any scenario where you need to explore the solution space rather than pick one metric to optimize.

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last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill pymoo --agent claude-code

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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

SKILL.md on GitHub