pymoo
Multi-Objective Optimization in Python
Decision gist · record as of 2026-08-14
Yes. pymoo is actively maintained, has no known vulnerabilities, supports current Python versions (3.10+), and fills a clear need for multi-objective optimization in Python. The medium install friction is justified by pre-built wheels and optional performance compilation. Suitable for research, engineering, and production use under Apache-2.0 licensing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; compiled extensions are optional but recommended for performance.
- Medium install friction due to compiled C extensions for performance; wheels are pre-built for Python 3.10–3.12 on major platforms (macOS, Linux, Windows).
- Active maintenance with a release 47 days ago.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most projects.
last release 2026-06-28 (47 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 880,245 downloads/mo, #4,822 on PyPI
Alternatives
Verify before relying
pip install pymoo
from pymoo.algorithms.moo.nsga2 import NSGA2
from pymoo.problems import get_problem
from pymoo.optimize import minimize
problem = get_problem("zdt1")
algorithm = NSGA2(pop_size=100)
res = minimize(problem, algorithm, ('n_gen', 200), seed=1)- Whether compiled extensions are automatically used or require explicit configuration after install.
- Performance characteristics compared to other multi-objective optimization frameworks.
- Scalability limits for problem size or population size in real-world applications.
What it is and what it does
pymoo is a framework for single- and multi-objective optimization that provides state-of-the-art algorithms (such as NSGA2) alongside utilities for visualization and decision-making. It depends on numpy, scipy, and several specialized libraries (moocore, autograd, cma) to handle numerical computation and algorithm-specific operations. The package includes optional compiled extensions for performance-critical sections, which can be verified after installation.
Developers use pymoo to solve optimization problems where multiple conflicting objectives must be balanced—finding Pareto-optimal solutions rather than a single best answer. It is designed for researchers and practitioners working with evolutionary algorithms, constraint handling, and multi-objective problem benchmarking. The framework handles problem definition, algorithm selection, execution, and result visualization within a unified interface.
Use it for
- Solve multi-objective engineering design problems where trade-offs between competing goals (cost, weight, performance) must be explored.
- Benchmark and compare evolutionary algorithms on standard test problems using built-in problem definitions.
- Visualize Pareto fronts and decision spaces to understand solution trade-offs in optimization results.
- Implement custom optimization algorithms by extending pymoo's base classes and algorithm interfaces.
- Research and prototype new multi-objective optimization techniques with access to standard algorithm implementations.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
pymoo is actively maintained, has no known vulnerabilities, supports current Python versions (3.10+), and fills a clear need for multi-objective optimization in Python. The medium install friction is justified by pre-built wheels and optional performance compilation. Suitable for research, engineering, and production use under Apache-2.0 licensing.
Install
pymoo on PyPI
Before you install
Medium install friction due to compiled C extensions for performance; wheels are pre-built for Python 3.10–3.12 on major platforms (macOS, Linux, Windows). Active maintenance with a release 47 days ago.
Requires Python 3.10 or later; compiled extensions are optional but recommended for performance.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most projects.
Quickstart
pip install pymoo
from pymoo.algorithms.moo.nsga2 import NSGA2
from pymoo.problems import get_problem
from pymoo.optimize import minimize
problem = get_problem("zdt1")
algorithm = NSGA2(pop_size=100)
res = minimize(problem, algorithm, ('n_gen', 200), seed=1)Verify before relying
- Whether compiled extensions are automatically used or require explicit configuration after install.
- Performance characteristics compared to other multi-objective optimization frameworks.
- Scalability limits for problem size or population size in real-world applications.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 8 packagesnumpyscipymoocoreautogradcmamatplotlibalive_progressDeprecated |
| Maintenance | Actively maintained 47 days since the last release |
| First released | |
| Downloads | 880,245 / month, #4,822 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Mathematics |
Evidence: pymoo-0.6.2-cp310-cp310-macosx_10_9_universal2.whl; pymoo-0.6.2-cp310-cp310-macosx_11_0_arm64.whl; pymoo-0.6.2-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; pymoo-0.6.2-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; pymoo-0.6.2-cp310-cp310-musllinux_1_2_aarch64.whl; pymoo-0.6.2-cp310-cp310-musllinux_1_2_x86_64.whl; pymoo-0.6.2-cp310-cp310-win_amd64.whl; pymoo-0.6.2-cp311-cp311-macosx_10_9_universal2.whl; pymoo-0.6.2-cp311-cp311-macosx_11_0_arm64.whl; pymoo-0.6.2-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; pymoo-0.6.2-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; pymoo-0.6.2-cp311-cp311-musllinux_1_2_aarch64.whl; pymoo-0.6.2-cp311-cp311-musllinux_1_2_x86_64.whl; pymoo-0.6.2-cp311-cp311-win_amd64.whl; pymoo-0.6.2-cp312-cp312-macosx_10_13_universal2.whl; pymoo-0.6.2-cp312-cp312-macosx_11_0_arm64.whl; pymoo-0.6.2-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; pymoo-0.6.2-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; pymoo-0.6.2-cp312-cp312-musllinux_1_2_aarch64.whl; pymoo-0.6.2-cp312-cp312-musllinux_1_2_x86_64.whl
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