关键词: 磷虾群算法, 余弦递减策略, 广义反向学习, 数据聚类, K均值聚类算法

Abstract: In order to solve the problem of premature convergence caused by the decrease of population diversity in the optimization process of Krill Herd (KH) algorithm, an improved krill herd algorithm based on Generalized Opposition-Based Learning was proposed, namely GOBL-KH. Firstly, step size factors were determined by cosine decreasing strategy to balance the exploration and exploitation ability of the algorithm. Then, a generalized opposition-based learning strategy was added to search each krill, which enhanced the ability of the krill to explore the neighborhood space around it. The proposed algorithm was tested on fifteen benchmark functions and compared with the original KH algorithm, KH with Linear Decreasing step (KHLD) and KH with Cosiner Decreasing step (KHCD). The experimental results show that the proposed algorithm can effectively avoid premature and has higher accuracy. In order to demonstrate the effectiveness of the proposed algorithm, it was combined with K-means algorithm to solve the data clustering problem, namely HK-KH. In this fusion algorithm, after each iteration, the worst individual was replaced by the optimal individual or a new individual after the K-means iteration. Five datasets of UCI were used to test HK-KH algorithm and the results were compared with the K-means, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), KH, KH Clustering Algorithm (KHCA), Improved KH (IKH) algorithm for clustering problems. The experimental results show that HK-KH algorithm is suitable to solve the data clustering problem and has strong global convergence and high stability.

Key words: Krill Herd (KH) algorithm, cosine decreasing strategy, generalized opposition-based learning, data clustering, K-means clustering algorithm