Clustering using an improved krill herd algorithm

Abstract

In recent years, metaheuristic algorithms have been widely used in solving clustering problems because of their good performance and application effects. krill herd algorithm (KHA) is a new effective algorithm to solve optimization problems based on the imitation of krill individual behavior, and it is proven to perform better than other swarm intelligence algorithms. However, there are some weaknesses yet. In this paper, an improved krill herd algorithm (IKHA) is studied. Modified mutation operators and updated mechanisms are applied to improve global optimization, and the proposed IKHA can overcome the weakness of KHA and performs better than KHA in optimization problems. Then, KHA and IKHA are introduced into the clustering problem. In our proposed clustering algorithm, KHA and IKHA are used to find appropriate cluster centers. Experiments were conducted on University of California Irvine (UCI) standard datasets, and the results showed that the IKHA clustering algorithm is the most effective.