An ACO Algorithm with Adaptive Volatility Rate of Pheromone Trail

Authors:
Zhifeng Hao;Han Huang;Yong Qin;Ruichu Cai
Affiliations:
College of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, P.R. China and National Mobile Communications Research Laboratory, Southeast University, Nanjin ...;College of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, P.R. China;Center of Information and Network, Maoming University, Maoming, Guangdong, 525000, P.R. China;College of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, P.R. China
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Year:
2007

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Abstract

Ant colony optimization (ACO) has been proved to be one of the best performing algorithms for NP-hard problems as TSP. The volatility rate of pheromone trail is one of the main parameters in ACO algorithms. It is usually set experimentally in the literatures for the application of ACO. The present paper proposes an adaptive strategy for the volatility rate of pheromone trail according to the quality of the solutions found by artificial ants. The strategy is combined with the setting of other parameters to form a new ACO algorithm. Finally, the experimental results of computing traveling salesman problems indicate that the proposed algorithm is more effective than other ant methods.