Swarm Seek

Literature-validated Artificial Bee Colony (ABC) optimization for Python.

Swarm Seek implements Original ABC and principal peer-reviewed continuous variants (GABC, qABC, MABC) with a NumPy core, an ask/tell API, and automated checks against published benchmark figures.

Status: 0.1.1 (beta). Continuous ABC core, variants, ask/tell API, and literature oracles. Docs: https://swarm-seek.readthedocs.io/

Install

Requires Python 3.10+.

pip install swarm-seek

Editable install for development:

pip install -e ".[dev]"

Quick start

from swarm_seek import ABC, ContinuousSpace
from swarm_seek.benchmarks import sphere

space = ContinuousSpace([(-5.0, 5.0)] * 5)
colony = ABC(space, variant="original", pop_size=20, limit=100, max_evals=5_000, seed=0)
best = colony.minimize(sphere)
print(best.fitness)

Ask/tell loop:

import numpy as np

from swarm_seek import ABC, ContinuousSpace
from swarm_seek.benchmarks import sphere

space = ContinuousSpace([(-5.0, 5.0)] * 5)
colony = ABC(space, variant="gabc", pop_size=20, limit=100, max_evals=5_000, seed=0)

while not colony.converged:
    candidates = colony.ask()
    if candidates.size == 0:
        colony.tell([])
        continue
    colony.tell(np.asarray(sphere(candidates), dtype=np.float64))

print(colony.best.fitness)

Runnable notebooks with stated pass criteria live in examples/. Full documentation (variants, architecture, API, FAQ) builds from docs/source/.

License

Apache License 2.0. See LICENSE.

Citation

See CITATION.cff for citation metadata.

Project links

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Newer release available (0.2.0)

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Credits

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Author: Travis Kessler

GitHub Statistics

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License expression

Apache-2.0
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Requires

Python >=3.10

Provides Extra

dev docs numba jax sklearn optuna

Tags

artificial-bee-colony abc swarm-intelligence optimization metaheuristic ask-tell

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