Optimizing Pathfinding: A Confluence of Ant Colony Optimization and GBFS

This paper aims to enhance pathfinding processes to create more efficient travel routes across various domains. It contributes to the development of optimization methodologies, offering valuable insights into the effectiveness of hybrid algorithms when dealing with complex combinatorial problems. Our approach combines the adaptability of ACO (Ant Colony Optimization) with the informed search strategy of GBFS (Greedy Best-First Search). Through extensive experimentation and comparative analysis, we highlight the significant advantages of our hybrid framework when compared to other similar metaheuristic algorithms and heuristic search techniques.