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Discrete Dynamics in Nature and Society
Volume 2015 (2015), Article ID 194792, 12 pages
http://dx.doi.org/10.1155/2015/194792
Research Article

An Improved Animal Migration Optimization Algorithm for Clustering Analysis

1College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China
2Guangxi High School Key Laboratory of Complex System and Intelligent Computing, Nanning 530006, China

Received 14 June 2014; Revised 17 December 2014; Accepted 17 December 2014

Academic Editor: Josef Diblík

Copyright © 2015 Mingzhi Ma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Animal migration optimization (AMO) is one of the most recently introduced algorithms based on the behavior of animal swarm migration. This paper presents an improved AMO algorithm (IAMO), which significantly improves the original AMO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique and it is used in many fields. The well-known method in solving clustering problems is -means clustering algorithm; however, it highly depends on the initial solution and is easy to fall into local optimum. To improve the defects of the -means method, this paper used IAMO for the clustering problem and experiment on synthetic and real life data sets. The simulation results show that the algorithm has a better performance than that of the -means, PSO, CPSO, ABC, CABC, and AMO algorithm for solving the clustering problem.