J. Sun, S. W. Xiong, and F. M. Guo, “A new pheromone updating strategy in ant colony optimization,” Proceedings of 2004 International Conference on Machine Learning and Cybernetics, Vol. 1, pp. 620–625, 2004.

has been cited by the following article:

• TITLE: Ant Colony Optimization Based on Adaptive Volatility Rate of Pheromone Trail

  AUTHORS: Zhaoquan CAI, Han HUANG, Yong QIN, Xianheng MA

  KEYWORDS: Ant Colony Optimization (ACO), Adaptive Volatility Rate, Pheromone Trail

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.2 No.8, November 17, 2009

  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 first proposes an adaptive strategy for the volatility rate of pheromone trail according to the quality of the solutions found by artificial ants. Second, the strategy is combined with the setting of other parameters to form a new ACO method. Then, the proposed algorithm can be proved to converge to the global optimal solution. Finally, the experimental results of computing traveling salesman problems and film-copy deliverer problems also indicate that the proposed ACO approach is more effective than other ant methods and non-ant methods.