moocore
Core Algorithms for Multi-Objective Optimization
Decision gist · record as of 2026-08-14
Yes, if you work in multi-objective optimization or need fast implementations of dominance filtering and quality metrics. The library is actively maintained, has no known vulnerabilities, supports modern Python versions, and offers prebuilt wheels for common platforms. The LGPL-2.1-or-later license requires attention if you plan proprietary use. Medium install friction is acceptable for a compiled package with broad platform coverage.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; building from source requires a C/C++ compiler.
- Medium install friction due to compiled C/C++ components; prebuilt wheels available for Python 3.10+ on macOS, Linux (x86_64 and aarch64), and Windows.
- Active maintenance with recent releases.
License · maintenance · safety
LGPL-2.1-or-later (copyleft) — Licensed under LGPL-2.1-or-later (copyleft); derivative works and modifications must be distributed under compatible terms. Suitable for research and open-source projects; proprietary use requires careful licensing review.
last release 2026-07-12 (33 days) · last repo commit 2026-08-11 · 62 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,019,593 downloads/mo, #4,495 on PyPI
Alternatives
Verify before relying
pip install moocore
import moocore
# Example: filter dominated solutions from a set of objective vectors
result = moocore.filter_dominated(objectives)- Specific performance benchmarks or scalability limits for large objective spaces
- Whether the package supports GPU acceleration or distributed computation
- Detailed API documentation and available function signatures beyond the description excerpt
What it is and what it does
moocore is a Python library that implements fast algorithms for multi-objective optimization problems. It provides core mathematical functions for working with Pareto-optimal solutions, including generation and transformation of nondominated sets, identification of dominated vectors, and computation of quality metrics like hypervolume, epsilon, and IGD. The library also implements the empirical attainment function, which characterizes the probabilistic distribution of outcomes from stochastic optimization algorithms.
The package is built on compiled C/C++ backends for performance and wraps them with a Python interface using cffi, numpy, and platformdirs. It targets researchers and practitioners in operations research, multi-criteria decision-making, and evolutionary optimization who need efficient implementations of these core algorithms. The library supports current Python versions (3.10 through 3.14) and is actively maintained.
Use it for
- Evaluate and compare multi-objective optimization algorithms by computing hypervolume and other quality indicators on their solution sets.
- Filter dominated solutions from a candidate set to identify the Pareto frontier in bi- or many-objective problems.
- Analyze stochastic optimization algorithm performance using empirical attainment functions and summary attainment surfaces.
- Build decision-support tools that need fast computation of dominance relationships and quality metrics.
- Research and benchmark evolutionary multi-objective optimization methods with standardized performance measures.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work in multi-objective optimization or need fast implementations of dominance filtering and quality metrics.
The library is actively maintained, has no known vulnerabilities, supports modern Python versions, and offers prebuilt wheels for common platforms. The LGPL-2.1-or-later license requires attention if you plan proprietary use. Medium install friction is acceptable for a compiled package with broad platform coverage.
Install
moocore on PyPI
Before you install
Medium install friction due to compiled C/C++ components; prebuilt wheels available for Python 3.10+ on macOS, Linux (x86_64 and aarch64), and Windows. Active maintenance with recent releases.
Requires Python 3.10 or later; building from source requires a C/C++ compiler.
License in practice
Licensed under LGPL-2.1-or-later (copyleft); derivative works and modifications must be distributed under compatible terms. Suitable for research and open-source projects; proprietary use requires careful licensing review.
Quickstart
pip install moocore
import moocore
# Example: filter dominated solutions from a set of objective vectors
result = moocore.filter_dominated(objectives)Verify before relying
- Specific performance benchmarks or scalability limits for large objective spaces
- Whether the package supports GPU acceleration or distributed computation
- Detailed API documentation and available function signatures beyond the description excerpt
Package facts
| License | LGPL-2.1-or-later copyleft |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 3 packagescffinumpyplatformdirs |
| Maintenance | Actively maintained 33 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,019,593 / month, #4,495 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: Science/ResearchOperating System :: OS IndependentProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: moocore-0.3.2-cp310-abi3-macosx_10_9_universal2.whl; moocore-0.3.2-cp310-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; moocore-0.3.2-cp310-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; moocore-0.3.2-cp310-abi3-musllinux_1_2_aarch64.whl; moocore-0.3.2-cp310-abi3-musllinux_1_2_x86_64.whl; moocore-0.3.2-cp310-abi3-win_amd64.whl; moocore-0.3.2-cp310-abi3-win_arm64.whl
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