A selfish herd optimization algorithm based on the simplex method for clustering analysis

Clustering analysis is a popular data analysis technology that has been successfully applied in many fields, such as pattern recognition, machine learning, image processing, data mining, computer vision and fuzzy control. Clustering analysis has made great progress in these fields. The purpose of clustering analysis is to classify data according to their intrinsic attributes such that data that have the same characteristics are in the same class and data that differ are in different classes. Currently, the k-means clustering algorithm is one of the most commonly used clustering methods because it is simple and easy to implement. However, its performance largely depends on the initial solution, and it easily falls into locally optimal solutions during the execution of the algorithm. To overcome the shortcomings of k-means clustering, many scholars have used meta-heuristic optimization algorithms to solve data clustering problems and have obtained satisfactory results. Therefore, in this paper, a selfish herd optimization algorithm based on the simplex method (SMSHO) is proposed. In SMSHO, the simplex method replaces mating operations to generate new prey individuals. The incorporation of the simplex method increases the population diversity of algorithm, thereby improving the global searching ability of algorithm. Twelve clustering datasets are selected to verify the performance of SMSHO in solving clustering problems. The SMSHO is compared with ABC, BPFPA, DE, k-means, PSO, SMSSO and SHO. The experimental results show that SMSHO has faster convergence speed, higher accuracy and higher stability than the other algorithms.