Swarm algorihtm are optimization algorithmes that takes inspiration from the swarm behaviours of natural creatures in order to apply it to solve optimization problems more efficietly. From entities as small as an E.coli bacteria, researchers have been able to deduce mathematical relationships that are prone to solve one of todays prominent AI problems, which is the minimization of complexe multimodal functions.
E.coli bacteria exhibit sophisticated foraging behavior to locate, consume, and process nutrients. This behavior can be divided into four main processes:
- Chemotaxis: Movement of bacteria towards higher nutrient concentrations. Bacteria can tumble (change direction randomly) or swim (move in a direction).
- Swarming: Bacteria form a swarm to move collectively towards favorable environments.
- Reproduction: The population of bacteria increases in size by splitting the healthiest bacteria.
- Elimination and Dispersal: Bacteria are eliminated in some areas and dispersed to other areas to explore new environments.
This repository contains two notebooks:
"Adaptive BFO.ipynb" : Contains a comparative study of the bacterial foraging algorithm and its adaptive variants.
"BFO notebook finale.ipynb" : Contains a comparison between BFO and SGD as well as a direct application of BFO in sentiment analysis.
For more information you can contact me via email : yassichirri@gmail.com or my linkedin in the bio, I'll be sure to help out. Take care!