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Entropy 2016, 18(5), 185; doi:10.3390/e18050185

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

1
,
1,2,* and 1,*
1
College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
2
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]   |  

Abstract

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

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