Nowadays, the problem of mobile robots to reach target points with optimum cost has become an important field of study. Although the optimum cost varies in studies, in general, time spent, distance, energy spent, or different combinations in which these are evaluated together. Especially in complex environments with a large number of obstacles, improvements are generally made on the algorithm side in order to solve the problems in acceptable time. This study focuses on improvement on the problem side. In this context, a hybrid model is proposed in which a metaheuristic algorithm and a clustering algorithm are used together in order to reduce the complexity of the environment by clustering the obstacles in static and two-dimensional environments and thus increase the running speed of optimization algorithms. For the proposed model, a detailed analysis has been performed first. In this analysis, particle swarm optimization (PSO) as metaheuristic algorithm and k-means clustering algorithm are used as clustering algorithm. As a result of this analysis, which was tested with various clustering rates, as the clustering rate increased, small losses were obtained in terms of shortest distance path, but the running speed of the algorithm increased at a level that could compensate these losses. In addition, effectiveness of the proposed model was evaluated comparatively on different metaheuristic and clustering algorithms. The results show that speed of path planning algorithms can be increased by the proposed model for two-dimensional environments with a large number of randomly located obstacles.