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name: pymoo description: "Use pymoo for single-, multi-, and many-objective optimization, custom problems, evolutionary operators, performance tuning, and Pareto analysis." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

pymoo

Use this repo skill when a task involves pymoo, evolutionary computation, single-objective optimization, multi-objective or many-objective optimization, Pareto fronts, custom optimization problems, genetic operators, reference directions, constraint handling, performance indicators, or pymoo visualization.

pymoo is a Python package for optimization workflows. It minimizes all objectives, represents inequality constraints as G <= 0, and centers common usage around a Problem, an Algorithm, a termination criterion, and pymoo.optimize.minimize.

Quick install and smoke

Public install path:

python -m pip install -U pymoo

Minimal import/runtime check:

python - <<'PY'
from pymoo.algorithms.moo.nsga2 import NSGA2
from pymoo.optimize import minimize
from pymoo.problems import get_problem

res = minimize(get_problem("zdt1"), NSGA2(pop_size=20), ("n_gen", 3), seed=1, verbose=False)
print(res.F.shape, res.algorithm.evaluator.n_eval)
PY

For a bundled self-check after installing pymoo, run scripts/pymoo_quickstart_smoke.py.

Route by task

User task signal	Read
Run NSGA-II/NSGA-III/GA/DE/PSO/MOEA/D, choose an algorithm, set termination, use minimize, inspect Result, callbacks, history, ask-and-tell, external evaluation loop	optimization-workflows
Define a custom problem, choose Problem vs ElementwiseProblem vs FunctionalProblem, use get_problem, fix F/G/H shapes, convert maximization or constraints, handle bounds/Pareto fronts	problem-modeling
Configure real/integer/binary/choice variables, mixed-variable search, sampling/crossover/mutation/repair, duplicate elimination, initial populations, custom object/permutation operators, hyperparameter helper APIs	operators-and-variables
Speed up slow evaluations, vectorize objectives, add thread/process starmap, use optional joblib/dask/ray, check Cython compiled extensions, reason about optional GPU/backend paths, control history/progress overhead	performance-and-parallelization
Compute hypervolume, GD/IGD/epsilon/KKTPM/R-metric, generate reference directions, use decomposition or MCDM, analyze convergence/history, save headless Pareto plots	analysis-and-visualization

Operating rules that prevent common pymoo mistakes

1. Minimization only: negate objectives that are originally maximized.
2. Constraint sign: inequality constraints are feasible when out["G"] <= 0; equality residuals belong in out["H"] with n_eq_constr.
3. Validate problem shapes first: check tiny problem.evaluate(...) samples before tuning algorithms.
4. Use explicit budgets: pass termination tuples such as ("n_gen", 50) or ("n_evals", 5000) for reproducible scripts.
5. Seed stochastic runs: pass seed=... and control randomness inside custom objective code, especially under parallel evaluation.
6. Match algorithm to search space: mixed/discrete/permutation variables need matching variables/operators or repair, not just a float-coded algorithm.
7. Keep optional extras explicit: base pymoo includes matplotlib and core numerical dependencies; joblib/dask/ray, Optuna, COMO-CMA-ES, pyrecorder, and GPU frameworks are optional task-specific additions.
8. Use headless plotting in agents: set a non-interactive Matplotlib backend and save figures instead of relying on GUI show().

Shared references

• API entrypoints: cross-sub-skill import map, version/extras notes, and first checks.
• Troubleshooting: install/import, compiled extension, optional dependency, and cross-workflow failure routing.
• Repo provenance: source version and evidence baseline for refresh decisions.
• Repo routing metadata: structured router metadata consumed during managed import.

Bundled self-check scripts

• scripts/pymoo_quickstart_smoke.py: base import, compiled-extension status, and tiny NSGA2/ZDT1 run.
• Sub-skill scripts cover custom-problem shape validation, mixed-variable search, parallel elementwise evaluation, indicator checks, compiled-extension checks, and headless scatter plotting.

Avoid this skill when

• The task is generic mathematical optimization with SciPy and does not use pymoo APIs, evolutionary operators, Pareto fronts, or pymoo problem classes.
• The task is hyperparameter optimization with Optuna/Ray Tune alone and no pymoo algorithm/problem integration.
• The task requires training deep learning models or GPU tensor code unrelated to pymoo's optional vectorized objective pattern.