Application of Basic Ant Colony Algorithm in Wireless Sensor Networks

In wireless sensor networks, the basic ant colony algorithm serves as a widely adopted optimization technique. This algorithm mimics the foraging behavior of ants to discover optimal solutions through pheromone-based path selection and probability-driven exploration. To enhance algorithmic performance, we integrate clustering algorithms that partition the network into multiple clusters, each managed by a centralized cluster head node. This hybrid approach significantly reduces energy consumption by minimizing long-distance transmissions and optimizing data aggregation paths, thereby extending the network's operational lifespan. The implementation typically involves key functions such as pheromone initialization, probability calculation for path selection using Roulette Wheel selection, and dynamic pheromone update mechanisms. By combining the basic ant colony algorithm with clustering strategies, we achieve superior network optimization through coordinated cluster formation and energy-efficient routing path establishment.