Basic Bacterial Foraging Clustering Algorithm

In this documentation, we explore the fundamental bacterial foraging clustering algorithm and utilize the IRIS dataset as our testbed. The bacterial foraging clustering algorithm is a biologically-inspired optimization method designed for data classification and clustering tasks, which assists in identifying patterns and relationships within datasets. By implementing this algorithm with the IRIS dataset, we validate its effectiveness and investigate result variations under different parameter configurations. The implementation typically involves three key phases: chemotaxis (directional movement), reproduction (population update based on fitness), and elimination-dispersal (maintaining diversity). Key parameters include step size for bacterial movement, reproduction threshold, and dispersion probability. This analysis enhances our understanding of the algorithm's practical applications, providing valuable insights for research and implementation scenarios. The code structure generally consists of initialization functions for bacterial populations, fitness evaluation methods using clustering metrics, and iterative optimization loops containing the core foraging behaviors.