Advertisement

ACO with Fuzzy Pheromone Laying Mechanism

  • Liu Yu
  • Jian-Feng Yan
  • Guang-Rong Yan
  • Lei Yi
Part of the Communications in Computer and Information Science book series (CCIS, volume 304)

Abstract

Pheromone laying mechanism is an important aspect to affect performance of ant colony optimization (ACO) algorithms. In most existing ACO algorithms, either only one best ant is allowed to release pheromone, or all the ants are allowed to lay pheromone in the same way. To make full use of ants to explore high quality routes, a fuzzy pheromone laying mechanism is proposed in the paper. The amount of ants that are allowed to lay pheromone varies at each iteration to differentiate different contributions of the ants. The experimental results show that the proposed algorithm possesses high searching ability and excellent convergence performance in comparison with the classic ACO algorithm.

Keywords

ant colony optimization (ACO) fuzzy pheromone laying 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dorigo, M., Birattari, M., Stutzle, T.: Ant Colony Optimization. IEEE Computational Intelligence Magazine 1(4), 28–39 (2006)Google Scholar
  2. 2.
    Deneubourg, J.L., et al.: The Self-organizing Exploratory Pattern of the Argentine ant. Journal of Insect Behavior 3(2), 159–168 (1990)CrossRefGoogle Scholar
  3. 3.
    Khan, S., Engelbrecht, A.: A Fuzzy Ant Colony Optimization Algorithm for Topology Design of Distributed Local Area Networks. In: 2008 IEEE Swarm Intelligence Symposium. IEEE, St. Louis (2008)Google Scholar
  4. 4.
    Donati, A.V., et al.: Time Dependent Vehicle Routing Problem with a Multi Ant Colony System. European Journal of Operational Research 185(3), 1174–1191 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Martens, D., et al.: Classification with Ant Colony Optimization. IEEE Transactions on Evolutionary Computation 11(5), 651–665 (2007)CrossRefGoogle Scholar
  6. 6.
    Chen, W.N., et al.: Optimizing Discounted Cash Flows in Project Scheduling–An Ant Colony Optimization Approach. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 40(1), 64–77 (2010)CrossRefGoogle Scholar
  7. 7.
    Picard, D., Revel, A., Cord, M.: An Application of Swarm Intelligence to Distributed Image Retrieval. Information Sciences (2010)Google Scholar
  8. 8.
    Chen, W.N., Zhang, J.: An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem with Various QoS Requirements. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 39(1), 29–43 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Liu Yu
    • 1
  • Jian-Feng Yan
    • 2
  • Guang-Rong Yan
    • 1
  • Lei Yi
    • 1
  1. 1.School of Mechanical Engineering and AutomationBeihang UniversityBeijingChina
  2. 2.School of Computer Science & TechnologySoochow UniversitySoochowChina

Personalised recommendations