An improved simplified mean particle swarm optimization K-means clustering algorithm

To figure out the problems such as the fact that particle swarm optimization algorithm is easy to fall into local optimum and K-means algorithm is greatly influenced by the number of clusters and the selection of initial cluster centers, an improved simplified mean particle swarm optimization K-means clustering algorithm (ISMPSO-AKM) is proposed. On the one hand, on the basis of simplified particle swarm optimization, the neighborhood optimal particle is added to improve the position formula by linear combination of individual optimal position, global optimal position and neighborhood optimal position. On the other hand, an inertia weight based on cosine function and logarithmic function is constructed to realize dynamic adjustment of inertia weight. In addition, AKM clustering algorithm is introduced to determine the number of clusters and dynamically obtain the initial center, which further improves the accuracy of the algorithm. The simulation results show that the improved ISMPSO-AKM algorithm has faster convergence speed, higher accuracy and more stable clustering results.