This paper investigates the use of iterative refinement in multi-agent pathfinding (MAPF), a path-planning problem for multiple robots. We analyze and compare several path planning algorithms, including ICBS, HCA, PIBT, CBS, and WHCA, to identify the most effective approach to finding optimal paths for multiple agents. Using these algorithms, we develop a suboptimal MAPF solver that quickly generates initial solutions, which we then refined through an iterative selection of a subset of agents and the application of an optimal MAPF solver. Our study provides valuable insights into the effectiveness of iterative refinement in MAPF and can help researchers identify the most efficient approach to this problem. These findings have practical applications in areas such as robotics, logistics, and transportation, where efficient path planning for multiple agents is essential for achieving optimal performance. This paper investigates the use of iterative refinement in multi-agent path-finding (MAPF), a path-planning problem for multiple robots. We analyse and compare several path-planning algorithms, including ICBS, HCA, PIBT, CBS, and WHCA, to identify the most effective approach to finding optimal paths for multiple agents. Using these algorithms, we develop a sub-optimal MAPF solver that quickly generates initial solutions, which we then refined through an iterative selection of a subset of agents and the application of an optimal MAPF solver. Our study provides valuable insights into the effectiveness of iterative refinement in MAPF and can help researchers identify the most efficient approach to this problem. The process of determining an optimal or near-optimal path between two points in a given environment while avoiding obstacles and other limitations is known as path planning. It is a significant issue in several areas, including robotics, autonomous cars, computer graphics, and video games. The goal of path planning is to find a feasible path that meets the following criteria: it connects the starting point to the goal point, it avoids obstacles and other environmental constraints, it optimises a specific performance metric such as distance, time, or energy, and it takes into account the dynamic nature of the environment and any changes that may occur during execution. These findings have practical applications in agents that are essential for achieving optimal performance