The Rise of Artificial Bee Colony Algorithms in Data Science and Machine Learning is Notable

Artificial Bee Colony (ABC) method optimization is based on honeybee foraging. This algorithm finds near-optimal solutions in complex search spaces in engineering, data mining, and Artificial Intelligence (AI). The versatile Honeybee Algorithm solves complex optimization problems in data science. It helps with feature selection, parameter optimization, network design, time-series analysis, anomaly detection, and load balancing. This is useful in machine learning, where iteratively exploring feature combinations improves model performance. Complex neural network parameters can be optimized by the algorithm. It can help design and operate telecommunications and transportation networks and identify time-series data patterns. It improves model accuracy by focusing on key features, optimizing hyperparameters, choosing the best model from a set of candidates, combining model output to improve forecasts, and clustering and organizing data points. Optimization of network structures, learning rates, and learning algorithms aids neural network training. The Honey Bee algorithm balances exploring and exploiting machine learning. It helps navigate complex, multidimensional environments. This chapter presents recent advancements in ABC algorithms applications in the field of data science and machine learning.