# anyoptimization/pymoo

NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO

Repository: https://github.com/anyoptimization/pymoo
Canonical: https://ross.abutalabs.com/products/pymoo
Homepage: https://pymoo.org
Language: Python
License: Apache-2.0
License Family: permissive
Topics: optimization, multi-objective-optimization, nsga2, nsga3, genetic-algorithm, differential-evolution, cmaes, pso
Last push: 2026-07-07T01:34:50+00:00

## Health v2 (maintenance only)
Score: 81/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 91, release rhythm 58, longevity 100
- inputs: {"age_days": 3270, "days_push": 58, "days_rel": 66, "gap_med": 198.5, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2946, forks 478 (observed 2026-08-28T04:07:31.226406+00:00)

## What it is
pymoo is an open-source Python framework for single-, multi-, and many-objective optimization, offering state-of-the-art algorithms like NSGA-II, NSGA-III, MOEA/D, GA, DE, CMA-ES, and PSO. It also includes problem definitions, visualization, parallelization, and decision-making tools for the full optimization workflow.

## Use cases
- run NSGA-II on a multi-objective optimization problem in Python
- compute a Pareto front for an engineering design problem
- solve constrained optimization problems with evolutionary algorithms
- apply differential evolution or CMA-ES to a single-objective function
- benchmark algorithms on ZDT, DTLZ, or WFG test problems
- visualize and analyze convergence of optimization runs
- parallelize objective function evaluations across cores

## When to choose
- you need evolutionary multi-objective optimization in Python
- you want a mature, well-documented library with many algorithms and test problems
- you need visualization, decision making, and parallel evaluation in one framework

## When to avoid
- you need gradient-based or convex optimization solvers
- you work outside Python or need a GUI-driven tool
- your problem is small enough for scipy.optimize

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, math, data-science, developer-tools
- domain: mathematics, data-science, performance
- platform: python, cross-platform
- tags: multi-objective-optimization, evolutionary-algorithms, nsga2, nsga3, genetic-algorithm, differential-evolution, cmaes, pso, pareto-front, metaheuristics, algorithms

## Member repositories
- anyoptimization/pymoo (main) score 81

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:31.226406+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:32:47.332049+00:00, confidence not recorded.
  - readme: https://github.com/anyoptimization/pymoo (fetched 2026-08-28T04:07:31.226406+00:00, sha cc132ad2ebfd)
  - homepage: https://pymoo.org (fetched 2026-08-29T09:48:09.079730+00:00, sha 80538c50d74b)
  - site_page: https://pymoo.org/installation.html (fetched 2026-08-29T09:48:09.088986+00:00, sha 8359748b0365)
  - site_page: https://pymoo.org/faq.html (fetched 2026-08-29T09:48:09.090885+00:00, sha 89e91d1e4d57)
  - site_page: https://pymoo.org/versions.html (fetched 2026-08-29T09:48:09.092460+00:00, sha eab2afa2e6b9)
- Data as of 2026-08-30T08:39:29.467469+00:00.