Artificial Bee Colony

Artificial Bee Colony (ABC) algorithms are nature-inspired metaheuristic optimization techniques mimicking the foraging behavior of honeybees, primarily used to solve complex optimization problems across diverse fields. Current research focuses on enhancing ABC's performance through hybridization with other algorithms like genetic programming, quantum-inspired methods, and neural networks, particularly for applications in machine learning, including hyperparameter tuning for deep learning models and feature selection for intrusion detection systems. These improvements aim to address limitations such as slow convergence and local optima, leading to more efficient and accurate solutions in areas like biomedical image processing, analog circuit design, and public transit network optimization.