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Discrete Dynamics in Nature and Society
Volume 2013, Article ID 815193, 9 pages
Research Article

A Novel Ant Colony Optimization Algorithm for Large Scale QoS-Based Service Selection Problem

College of Information Science & Engineering, Northeastern University, Shenyang 110819, China

Received 11 March 2013; Revised 25 May 2013; Accepted 3 June 2013

Academic Editor: Manuel De la Sen

Copyright © 2013 Changsheng Zhang 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.


To tackle the large scale QoS-based service selection problem, a novel efficient clustering guided ant colony service selection algorithm called CASS is proposed in this paper. In this algorithm, a skyline query process is used to filter the candidates related to each service class, and a clustering based shrinking process is used to guide the ant to the search directions. We evaluate our approach experimentally using standard real datasets and synthetically generated datasets and compared it with the recently proposed related service selection algorithms. It reveals very encouraging results in terms of the quality of solution and the processing time required.