An improved ant colony algorithm and simulation

Authors:
Li Xin;Yu Datai;Qin Jin
Affiliations:
Information Engineering School, University of Science and Technology in Beijing, Beijing, China and College of Information Science and Engineering, Henan University of Technology, Zhengzhou, China;Information Engineering School, University of Science and Technology in Beijing, Beijing, China;Information Engineering School, University of Science and Technology in Beijing, Beijing, China and College of Computer Science and Technology, Guizhou University, Guiyang, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Abstract

We demonstrate a novel Ant Colony System with dynamically varied parameters and a penalty-reward function, which is based on the Basic Ant System (BAS) algorithm, also presented is its application to solving complex TSP problem. Our new algorithm has two important features, the first: a perturbation factor formulated by inverse exponent penalty-reward function is developed; the second: a corresponding transition strategy with random selection is designed. Numerical simulation demonstrates that our new algorithm has much higher convergence speed and stability than BAS algorithm, and brings along good effects of reducing CPU time, and preventing search from being in stagnation behavior.