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 LeonChaoX/qinyan-academic-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), 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 ## Core Concepts ### The Unified Interface Pymoo uses a consistent `minimize()` function for all optimization tasks: ```python
- Overview
- When to Use This Skill
- Core Concepts
- The Unified Interface
- Problem Types
- Quick Start Workflows
- Workflow 1: Single-Objective Optimization
- Workflow 2: Multi-Objective Optimization (2-3 objectives)
- Workflow 3: Many-Objective Optimization (4+ objectives)
- Workflow 4: Custom Problem Definition
- Workflow 5: Constraint Handling
- Workflow 6: Decision Making from Pareto Front
- Workflow 7: Visualization
- Algorithm Selection Guide
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 uv pip install pymoo
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 LeonChaoX/qinyan-academic-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 LeonChaoX/qinyan-academic-skills, a repository with 759 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.