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Scientific Programming
Volume 2016, Article ID 5136327, 12 pages
http://dx.doi.org/10.1155/2016/5136327
Research Article

AntStar: Enhancing Optimization Problems by Integrating an Ant System and Algorithm

College of Computer and Information Sciences (CCIS), King Saud University, P.O. Box 5117, Riyadh 11543, Saudi Arabia

Received 13 September 2015; Revised 17 December 2015; Accepted 24 December 2015

Academic Editor: Stéphane Caro

Copyright © 2016 Mohammed Faisal 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.

Abstract

Recently, nature-inspired techniques have become valuable to many intelligent systems in different fields of technology and science. Among these techniques, Ant Systems (AS) have become a valuable technique for intelligent systems in different fields. AS is a computational system inspired by the foraging behavior of ants and intended to solve practical optimization problems. In this paper, we introduce the AntStar algorithm, which is swarm intelligence based. AntStar enhances the optimization and performance of an AS by integrating the AS and algorithm. Applying the AntStar algorithm to the single-source shortest-path problem has been done to ensure the efficiency of the proposed AntStar algorithm. The experimental result of the proposed algorithm illustrated the robustness and accuracy of the AntStar algorithm.