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pymoo skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

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Install the pymoo skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/pymoo ~/.claude/skills/pymoo
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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

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 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:

from pymoo.optimize import minimize

result = minimize(
    problem,        # What to optimize
    algorithm,      # How to optimize
    termination,    # When to stop
    seed=1,
    verbose=True
)

Result object contains:

  • result.X: Decision variables of optimal solution(s)
  • result.F: Objective values of optimal solution(s)
  • result.G: Constraint violations (if constrained)
  • result.algorithm: Algorithm object with history

Problem Definition Styles

Pymoo supports three problem definition styles:

  • Problem: Vectorized — _evaluate receives a batch of solutions (matrix)
  • ElementwiseProblem: One solution per call — recommended for custom problems and parallel evaluation
  • FunctionalProblem: Define objectives and constraints as separate functions without subclassing

Problem Types

Single-objective: One objective to minimize/maximize Multi-objective: 2-3 conflicting objectives → Pareto front Many-objective: 4+ objectives → High-dimensional Pareto front Constrained: Objectives + inequality/equality constraints Mixed-variable: Continuous, integer, binary, and categorical variables in one problem Dynamic: Time-varying objectives or constraints

Quick Start Workflows

Nine runnable workflows are in references/quickstartworkflows.md:

Algorithm Selection Guide

Single-Objective Problems

Multi-Objective Problems (2-3 objectives)

Many-Objective Problems (4+ objectives)

Constrained Problems

See: references/algorithms.md for comprehensive algorithm reference

Benchmark Problems

Quick problem access:

from pymoo.problems import get_problem

# Single-objective
problem = get_problem("rastrigin", n_var=10)
problem = get_problem("rosenbrock", n_var=10)

# Multi-objective
problem = get_problem("zdt1")        # Convex front
problem = get_problem("zdt2")        # Non-convex front
problem = get_problem("zdt3")        # Disconnected front

# Many-objective
problem = get_problem("dtlz2", n_obj=5, n_var=12)
problem = get_problem("dtlz7", n_obj=4)

See: references/problems.md for complete test problem reference

Genetic Operator Customization

Standard operator configuration:

from pymoo.algorithms.soo.nonconvex.ga import GA
from pymoo.operators.crossover.sbx import SBX
from pymoo.operators.mutation.pm import PM

algorithm = GA(
    pop_size=100,
    crossover=SBX(prob=0.9, eta=15),
    mutation=PM(eta=20),
    eliminate_duplicates=True
)

Operator selection by variable type:

Continuous variables:

  • Crossover: SBX (Simulated Binary Crossover)
  • Mutation: PM (Polynomial Mutation)

Binary variables:

  • Crossover: TwoPointCrossover, UniformCrossover
  • Mutation: BitflipMutation

Permutations (TSP, scheduling):

  • Crossover: OrderCrossover (OX)
  • Mutation: InversionMutation

See: references/operators.md for comprehensive operator reference

Performance and Troubleshooting

Common issues and solutions:

Problem: Algorithm not converging

  • Increase population size
  • Increase number of generations
  • Check if problem is multimodal (try different algorithms)
  • Verify constraints are correctly formulated

Problem: Poor Pareto front distribution

  • For NSGA-III: Adjust reference directions
  • Increase population size
  • Check for duplicate elimination
  • Verify problem scaling

Problem: Few feasible solutions

  • Use constraint-as-objective approach
  • Apply repair operators
  • Try SRES/ISRES for constrained problems
  • Check constraint formulation (should be g <= 0)

Problem: High computational cost

  • Reduce population size
  • Decrease number of generations
  • Use simpler operators
  • Enable parallel evaluation via elementwise_runner (see Workflow 8)

Best practices:

  1. Normalize objectives when scales differ significantly
  2. Set random seed for reproducibility
  3. Save history to analyze convergence: save_history=True
  4. Visualize results to understand solution quality
  5. Compare with true Pareto front when available
  6. Use appropriate termination criteria (generations, evaluations, tolerance)
  7. Tune operator parameters for problem characteristics

Resources

This skill includes comprehensive reference documentation and executable examples:

references/

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