An ACO Algorithm with Adaptive Volatility Rate of Pheromone Trail

  • Zhifeng Hao
  • Han Huang
  • Yong Qin
  • Ruichu Cai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4490)


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.


Ant colony optimization pheromone trail adaptive volatility rate 


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Zhifeng Hao
    • 1
    • 2
  • Han Huang
    • 1
  • Yong Qin
    • 3
  • Ruichu Cai
    • 1
  1. 1.College of Computer Science and Engineering, South China University of Technology, Guangzhou 510640P.R. China
  2. 2.National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096P.R. China
  3. 3.Center of Information and Network, Maoming University, Maoming, Guangdong, 525000P.R. China

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