An Improved Animal Migration Optimization Algorithm for Clustering Analysis
Joint Authors
Li, Liangliang
Zhou, Yongquan
Ma, Mingzhi
Chen, Xin
Luo, Qifang
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-01-05
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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 k-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 k-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 k-means, PSO, CPSO, ABC, CABC, and AMO algorithm for solving the clustering problem.
American Psychological Association (APA)
Ma, Mingzhi& Luo, Qifang& Zhou, Yongquan& Chen, Xin& Li, Liangliang. 2015. An Improved Animal Migration Optimization Algorithm for Clustering Analysis. Discrete Dynamics in Nature and Society،Vol. 2015, no. 2015, pp.1-12.
https://search.emarefa.net/detail/BIM-1060405
Modern Language Association (MLA)
Ma, Mingzhi…[et al.]. An Improved Animal Migration Optimization Algorithm for Clustering Analysis. Discrete Dynamics in Nature and Society No. 2015 (2015), pp.1-12.
https://search.emarefa.net/detail/BIM-1060405
American Medical Association (AMA)
Ma, Mingzhi& Luo, Qifang& Zhou, Yongquan& Chen, Xin& Li, Liangliang. An Improved Animal Migration Optimization Algorithm for Clustering Analysis. Discrete Dynamics in Nature and Society. 2015. Vol. 2015, no. 2015, pp.1-12.
https://search.emarefa.net/detail/BIM-1060405
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1060405