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VAMOS

Vectorized Architecture for Multiobjective Optimization Studies

Multi-objective optimizationbuilt for reproducible studies.

A scientific Python framework with one optimization API, vectorized kernels, durable experiments, and explicit reproducibility contracts.

Get started API reference
$pip install vamos-optimization 
from vamos import optimize

result = optimize(
    "zdt1",
    algorithm="nsgaii",
    max_evaluations=400,
    seed=42,
)

print(result.F.shape)
one API · explicit seed · durable results
01

Try VAMOS

Install the package, optimize a built-in problem, and inspect the Pareto approximation in a few minutes.

Quickstart → 02

Solve my problem

Translate domain decisions and requirements into objectives, bounds, and constraints, then optimize them through the public problem builder.

Custom problems → 03

Run a reproducible study

Plan a problem–algorithm–seed matrix, verify its budget before execution, and trace every summary row back to canonical run evidence.

Reproducible studies →

Prefer runnable source? Run the three executable journeys.

Scientific workflows, not just algorithms

VAMOS 1.0 distinguishes stable optimization, run-artifact, and single-owner study surfaces from experimental features such as Studio, provider integrations, and tuning. Check Stability and versioning before depending on an API as a 1.x compatibility commitment.

AlgorithmsNSGA-II/III, MOEA/D, SMS-EMOA, SPEA2, IBEA, SMPSO, AGE-MOEA, RVEA.
EncodingsReal, integer, binary, permutation, and mixed decision variables where supported.
BackendsNumPy reference execution with optional Numba kernel and MooCore indicator acceleration.
Research toolingStudies, benchmarking, tuning, result inspection, verification, replay, and analysis.

For precise signatures and configuration contracts, use the API reference, algorithm reference, and problem reference.

Project and citation

Citation metadata is maintained in CITATION.cff. Security policy and supported reporting channels are maintained in SECURITY.md. See Known limitations, the roadmap, and repository governance for project-level information.