A MATLAB-Based Comparative Evaluation of ACO and A* in Dynamic Environments and PSO for Static Pathfinding

Pathfinding is a critical problem in robotics, artificial intelligence, and network optimization. This paper presents a comparative evaluation of three popular algorithms: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and A* search. Implemented in MATLAB, the study analyzes their performance in terms of path length, execution time, convergence behavior, and adaptability under both static and dynamic grid environments. Results indicate that A* consistently provides the shortest paths with minimal computation time, ACO demonstrates robustness in dynamic environments, and PSO offers a balance between accuracy and efficiency. The findings contribute to the selection of suitable algorithms for real-world applications in robotics, multi-agent systems, and intelligent navigation.