摘要：Particle Swarm Optimization (PSO) is a well known meta-heuristic that has been used in many applications for solving optimization problems. But it has some problems such as local minima. In this paper proposed an optimization algorithm called Movement Particle Swarm Optimization (MPSO) that enhances the behavior of PSO by using a random movement function to search for more points in the search space. The meta-heuristic has been experimented over 23 benchmark faction compared with state of the art algorithms: Multi-Verse Optimizer (MFO), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer (GWO) and particle Swarm Optimization (PSO). The Results showed that the proposed algorithm has enhanced the PSO over the tested benchmarked functions.
