Next Article in Journal
Quantum Thermodynamics in Strong Coupling: Heat Transport and Refrigeration
Previous Article in Journal
Geometric Model of Black Hole Quantum N-portrait, Extradimensions and Thermodynamics
Article Menu

Export Article

Open AccessArticle
Entropy 2016, 18(5), 185;

An Information Entropy-Based Animal Migration Optimization Algorithm for Data Clustering

1,2,* and 1,*
College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
Authors to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Received: 16 March 2016 / Revised: 3 May 2016 / Accepted: 10 May 2016 / Published: 16 May 2016
(This article belongs to the Section Information Theory)
View Full-Text   |   Download PDF [757 KB, uploaded 16 May 2016]   |  


Data clustering is useful in a wide range of application areas. The Animal Migration Optimization (AMO) algorithm is one of the recently introduced swarm-based algorithms, which has demonstrated good performances for solving numeric optimization problems. In this paper, we presented a modified AMO algorithm with an entropy-based heuristic strategy for data clustering. The main contribution is that we calculate the information entropy of each attribute for a given data set and propose an adaptive strategy that can automatically balance convergence speed and global search efforts according to its entropy in both migration and updating steps. A series of well-known benchmark clustering problems are employed to evaluate the performance of our approach. We compare experimental results with k-means, Artificial Bee Colony (ABC), AMO, and the state-of-the-art algorithms for clustering and show that the proposed AMO algorithm generally performs better than the compared algorithms on the considered clustering problems. View Full-Text
Keywords: animal migration optimization; information entropy; data clustering animal migration optimization; information entropy; data clustering

Figure 1

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. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Hou, L.; Gao, J.; Chen, R. An Information Entropy-Based Animal Migration Optimization Algorithm for Data Clustering. Entropy 2016, 18, 185.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top