Optimization of web page cache replacement strategy based on pheromone

In the context of rapidly growing web traffic and increasing demands for efficient data access, optimizing cache replacement strategies has become critical for enhancing web performance. Traditional algorithms such as LRU and ARC face challenges in adapting to diverse and dynamic access patterns, particularly under varying cache sizes. To address these limitations, this paper proposes a novel pheromone-based optimization approach inspired by ant colony optimization (ACO). Through extensive simulations, we demonstrate that ACO significantly outperforms LRU and ARC in smaller cache environments, achieving higher hit rates. In larger caches, the performance gap among ACO, ARC, and LRU is minimal, with ARC showing a slight advantage. These findings highlight the importance of adaptive strategies in web caching and underscore the potential of pheromone-based optimization to improve caching efficiency.