Competition Controlled Pheromone Update for Ant Colony Optimization

  • Daniel Merkle
  • Martin Middendorf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3172)


Pheromone information is used in Ant Colony Optimization (ACO) to guide the search process and to transfer knowledge from one iteration of the optimization algorithm to the next. Typically, in ACO all decisions that lead an ant to a good solution are considered as of equal importance and receive the same amount of pheromone from this ant (assuming the ant is allowed to update the pheromone information). In this paper we show that the decisions of an ant are usually made under situations with different strength of competition. Thus, the decisions of an ant do not have the same value for the optimization process and strong pheromone update should be prevented when competition is weak. We propose a measure for the strength of competition that is based on Kullback-Leibler distances. This measure is used to control the update of the pheromone information so that solutions components that correspond to decisions that were made under stronger competition receive more pheromone. We call this update procedure competition controlled pheromone update. The potential usefulness of competition controlled pheromone update is shown first on simple test problems for a deterministic model of ACO. Then we show how the new update method can be applied for ACO algorithms.


Problem Instance Solution Quality Cost Matrix Permutation Problem Pheromone Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Blum, C., Sampels, M.: Ant Colony Optimization for FOP Shop scheduling: A case study on different pheromone representations. In: Proc. of the 2002 Congress on Evolutionary Computation (CEC 2002), pp. 1558–1563 (2002)Google Scholar
  2. 2.
    Bullnheimer, B., Hartl, R.F., Strauss, C.: A new rank based version of the ant system - a computational study. Central Europ. J. Oper. Res. 7(1), 25–38 (1999)zbMATHMathSciNetGoogle Scholar
  3. 3.
    Dorigo, M.: Optimization, Learning and Natural Algorithms (in Italian). PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy (1992)Google Scholar
  4. 4.
    Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 11–32. McGraw-Hill, New York (1999)Google Scholar
  5. 5.
    Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Trans. Systems, Man, and Cybernetics – Part B 26, 29–41 (1996)CrossRefGoogle Scholar
  6. 6.
    Dorigo, M., Zlochin, M., Meuleau, N., Birattari, M.: Updating ACO Pheromones Using Stochastic Gradient Ascent and Cross-Entropy Methods. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds.) EvoIASP 2002, EvoWorkshops 2002, EvoSTIM 2002, EvoCOP 2002, and EvoPlan 2002. LNCS, vol. 2279, pp. 21–30. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Meuleau, N., Dorigo, M.: Ant colony optimization and stochastic gradient descent. Artificial Life 8(2), 103–121 (2002)CrossRefGoogle Scholar
  8. 8.
    Merkle, D., Middendorf, M.: An Ant Algorithm with a new Pheromone Evaluation Rule for Total Tardiness Problems. In: Oates, M.J., Lanzi, P.L., Li, Y., Cagnoni, S., Corne, D.W., Fogarty, T.C., Poli, R., Smith, G.D. (eds.) EvoIASP 2000, EvoWorkshops 2000, EvoFlight 2000, EvoSCONDI 2000, EvoSTIM 2000, EvoTEL 2000, and EvoROB/EvoRobot 2000. LNCS, vol. 1803, pp. 287–296. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  9. 9.
    Merkle, D., Middendorf, M.: A New Approach to Solve Permutation Scheduling Problems with Ant Colony Optimization. In: Boers, E.J.W., Gottlieb, J., Lanzi, P.L., Smith, R.E., Cagnoni, S., Hart, E., Raidl, G.R., Tijink, H. (eds.) EvoIASP 2001, EvoWorkshops 2001, EvoFlight 2001, EvoSTIM 2001, EvoCOP 2001, and EvoLearn 2001. LNCS, vol. 2037, pp. 213–222. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
    Merkle, D., Middendorf, M.: Ant colony optimization with the relative pheromone evaluation method. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds.) EvoIASP 2002, EvoWorkshops 2002, EvoSTIM 2002, EvoCOP 2002, and EvoPlan 2002. LNCS, vol. 2279, pp. 325–333. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Merkle, D., Middendorf, M.: Modelling the Dynamics of Ant Colony Optimization Algorithms. Evolutionary Computation 10(3), 235–262 (2002)CrossRefGoogle Scholar
  12. 12.
    Merkle, D., Middendorf, M.: Modelling ACO: Composed Permutation Problems. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 149–162. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Merkle, D., Middendorf, M., Schmeck, H.: Ant Colony Optimization for Resource-Constrained Project Scheduling. IEEE Transactions on Evolutionary Computation 6(4), 333–346 (2002)CrossRefGoogle Scholar
  14. 14.
    Randall, M., Tonkes, E.: Intensification and Diversification Strategies in Ant Colony Optimisation. TR00-02, School of Inf. Technology, Bond University (2000)Google Scholar
  15. 15.
    Stützle, T., Hoos, H.H.: MAX-MIN Ant System. Future Generation Computer Systems 16(8), 889–914 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Daniel Merkle
    • 1
  • Martin Middendorf
    • 1
  1. 1.Department of Computer ScienceUniversity of LeipzigLeipzigGermany

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