This research explores Swarm Intelligence algorithms, an advanced field in artificial intelligence that mimics the collective behavior of organisms like ants and bees to solve complex problems. These algorithms are characterized by decentralization, adaptability, and parallel processing, making them effective in optimization tasks.

The Ant Colony Optimization (ACO) algorithm models ant foraging behavior to solve path optimization problems, such as the Traveling Salesman Problem (TSP), showcasing scalability and gradual performance improvement. Similarly, the Bee Algorithm mimics bee foraging behavior for resource optimization and multi-dimensional search, offering efficiency in various applications.

A case study compares the performance of ACO and the Bee Algorithm in solving a real-world problem, such as optimizing transportation routes. The results evaluate their speed, accuracy, and ability to handle complexity, highlighting the practical potential of Swarm Intelligence in AI-driven solutions.