pymoo
Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
npx skills add K-Dense-AI/scientific-agent-skills --skill pymoo --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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. Current stable release: **pymoo 0.6.1.6** (November 2025). ## Installation ```bash uv pip install pymoo ``` For reproducible environments, pin a version: `uv pip install "pymoo==0.6.1.6"`. **Dependencies:** NumPy (2.x compatible since 0.6.1.3), SciPy, matplotlib (visualization). Autograd is optional for gradient-based features (since 0.6.1.3). **Documentation:** https://pymoo.org/ — LLM-friendly index: https://pymoo.org/llms.txt ## 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 pr
- Overview
- Installation
- When to Use This Skill
- Core Concepts
- The Unified Interface
- Problem Definition Styles
- Problem Types
- Quick Start Workflows
- Algorithm Selection Guide
- Single-Objective Problems
- Multi-Objective Problems (2-3 objectives)
- Many-Objective Problems (4+ objectives)
- Constrained Problems
- Benchmark Problems
uv pip install pymoo python3 scripts/single_objective_example.py python3 scripts/multi_objective_example.py python3 scripts/many_objective_example.py python3 scripts/custom_problem_example.py python3 scripts/decision_making_example.py
What does the pymoo skill do?
Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill pymoo --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.