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Computational Intelligence and Neuroscience
Volume 2014, Article ID 160730, 11 pages
Research Article

A Novel Artificial Immune Algorithm for Spatial Clustering with Obstacle Constraint and Its Applications

1College of National Territorial Resources and Tourism, Anhui Normal University, China
2Engineering Technology Research Center of Network and Information Security, Anhui Normal University, China
3Faculty of Health, Engineering and Sciences, University of Southern Queensland, Australia

Received 18 July 2014; Accepted 23 August 2014; Published 4 November 2014

Academic Editor: Jianjun Yang

Copyright © 2014 Liping Sun 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.


An important component of a spatial clustering algorithm is the distance measure between sample points in object space. In this paper, the traditional Euclidean distance measure is replaced with innovative obstacle distance measure for spatial clustering under obstacle constraints. Firstly, we present a path searching algorithm to approximate the obstacle distance between two points for dealing with obstacles and facilitators. Taking obstacle distance as similarity metric, we subsequently propose the artificial immune clustering with obstacle entity (AICOE) algorithm for clustering spatial point data in the presence of obstacles and facilitators. Finally, the paper presents a comparative analysis of AICOE algorithm and the classical clustering algorithms. Our clustering model based on artificial immune system is also applied to the case of public facility location problem in order to establish the practical applicability of our approach. By using the clone selection principle and updating the cluster centers based on the elite antibodies, the AICOE algorithm is able to achieve the global optimum and better clustering effect.