:bee: beecol

A simple Artificial Bee Colony (ABC) algorithm library for Python.

Features

• Pure Python implementation of the Artificial Bee Colony (ABC) algorithm
• Simple API for continuous optimization problems
• Easily extensible and customizable
• Only dependency: numpy

Installation

pip install beecol

Or clone this repository and use locally:

git clone https://github.com/atasoglu/beecol.git
cd beecol
pip install .

Usage

import numpy as np
from beecol import ArtificialBeeColony

# Define your fitness function (to maximize)
def fit_func(x):
    # Example: Sphere function (min at 0, but ABC maximizes, so use negative)
    return -np.sum(x**2)

# Set up the optimizer
abc = ArtificialBeeColony(
    fit_func=fit_func,
    dim=5,                # Number of parameters
    bounds=(-5, 5),       # Search space bounds
    n_bees=20,            # Number of bees (food sources)
)

# Run optimization
for i in range(100):
    best_solution, best_fitness = abc.step()
    print(f"Iteration {i}: Best fitness = {best_fitness}")

print("Best solution found:", best_solution)

Example: Knapsack Problem

A full example is provided in examples/knapsack.py, including plotting and a custom fitness function for the 0/1 knapsack problem.

To run:

cd examples
python knapsack.py

Citation

• Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.
• Karaboga, D., & Basturk, B. (2007). Artificial Bee Colony (ABC) optimization algorithm for solving constrained optimization problems. In Foundations of Fuzzy Logic and Soft Computing, LNCS 4529, 789–798.

Contributing

Contributions are welcome! Feel free to open issues or submit pull requests to help improve pygena.

License

MIT