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cmenpy
Compute Metaheuristic Engine for Python (cmenpy or /siːmɛnpaɪ/) is an extensible, flexible, and vectorized Python framework for designing and prototyping metaheuristic and population-based optimization algorithms.
It provides the clean abstractions necessary to build custom evolutionary algorithms, swarm intelligence methods, and hybrid heuristics from scratch — with absolute architectural control.
Installation
pip install cmenpy # runtime: NumPy + Numba only
Requires Python >=3.11.
To develop or build the docs:
git clone https://github.com/ltsim/cmenpy.git
cd cmenpy
uv sync --extra dev # tests, type checks, linters
uv sync --extra docs # ... plus the MkDocs toolchain
Quick example
import numpy as np
import cmenpy as cm
@cm.declare(x=cm.Argument[int, (0, 3), 2])
def my_algorithm(args, epoch, ctx):
pop = cm.PopulationSwarm(ctx.buff)
pop.compute << ctx.rng.uniform(
ctx.bounds.low, ctx.bounds.up, (pop.size, ctx.bounds.ndim)
)
# ... one epoch of your operator ...
best_fitness, best_x = my_algorithm(x=1).solve(
lambda x: np.sum(x**2), cm.Bounds([(-1, 1)] * 3), epochs=150, pop_size=15
)
See the Quickstart for both the function (@cm.declare) and class (@cm.template) styles.
Core pillars
- Vectorized data structures. Population arrays, agent states, and search spaces use a flattened, vectorized layout with no per-individual objects, interfacing natively with NumPy and Numba.
- Extensible architecture. Custom operators, mutation rules, or entirely new paradigms require overriding only a few decoupled, well-defined methods.
- Non-monolithic flexibility. No visualization tools, file exporters, or rigid loggers — just the mathematical engine and its structural flow.
- Research ready. Licensed under the GNU GPLv3. Design, modify, and distribute your algorithms freely; derivative works stay open, keeping the software a public good for research and commercial development.
Documentation
Full guides and the API reference are published at ltsim.github.io/cmenpy.
| Guide | Covers |
|---|---|
| Quickstart | Solve your first problem in minutes |
| Models | @cm.declare vs @cm.template, the ctx object |
| Build a custom model | Step-by-step tutorial implementing your own optimizer |
| Numba kernels | @cm.precompile, @cm.vectorize, @cm.reduce |
| Population | PopulationSwarm, buffers, selection, ranking |
| Target functions | Objectives, NFE budget, vectorized targets |
| Examples | Runnable algorithms in example/ |
| API reference | Full reference for every public symbol, by category |
| Changelog | Release notes |
To preview locally: uv sync --extra docs && .venv/bin/mkdocs serve.
Contributing
Contributions are welcome — see CONTRIBUTING.md and the list of contributors.
1 maintainer
Metadata
1 maintainer
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