As a part of my bachelor's thesis, I extended the NiaPy microframework [1], adding to it, the Bacterial Foraging Optimization algorithm as proposed by K. M. Passino in 2002 [2]. This repository contains the various experiments I ran, comparing the performance of BFO with other popular nature-inspired algorithms, specifically Differential Evolution, Particle Swarm Optimization and the Bat Algorithm.
The details of the experiments are described in the jupyter notebook bfo_experiments.ipynb.
The results themselves can be found in the results directory in the form of pickled pandas dataframes and LaTeX tables.
The code used to run the experiments is located in bfo_experiments.py.
For viewing purposes, the notebook can be viewed in github directly or with a third party tool, like nbviewer.
If you want to edit and run the notebook follow the instructions below:
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Clone this repository:
git clone https://github.com/zStupan/bfo-experiments cd bfo-experiments -
Create and activate a virtual environment:
python3 -m venv venv . venv/bin/activate # or "source venv/bin/activate"
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Install requirements:
pip install -r requirements.txt
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Start the jupyter notebook (should open browser tab):
jupyter notebook
Warning: Running the experiments using bfo_experiments.py takes a very long time (a little over 3 days on my machine) so I wouldn't recommend it.
[1] G. Vrbančič, L. Brezočnik, U. Mlakar, D. Fister in I. Fister, “NiaPy: Python microframework for building nature-inspired algorithms”, Journal of Open Source Software, vol. 3, no. 23, pp. 613, 2018.
[2] K. M. Passino, “Biomimicry of bacterial foraging for distributed optimization and control”, IEEE Control Systems Magazine, vol. 22, no. 3, pp. 52–67, 2002.