Abstract
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.
Keywords
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
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)
Dorigo, M.: Optimization, Learning and Natural Algorithms (in Italian). PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy (1992)
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)
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)
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)
Meuleau, N., Dorigo, M.: Ant colony optimization and stochastic gradient descent. Artificial Life 8(2), 103–121 (2002)
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)
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)
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)
Merkle, D., Middendorf, M.: Modelling the Dynamics of Ant Colony Optimization Algorithms. Evolutionary Computation 10(3), 235–262 (2002)
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)
Merkle, D., Middendorf, M., Schmeck, H.: Ant Colony Optimization for Resource-Constrained Project Scheduling. IEEE Transactions on Evolutionary Computation 6(4), 333–346 (2002)
Randall, M., Tonkes, E.: Intensification and Diversification Strategies in Ant Colony Optimisation. TR00-02, School of Inf. Technology, Bond University (2000)
Stützle, T., Hoos, H.H.: MAX-MIN Ant System. Future Generation Computer Systems 16(8), 889–914 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Merkle, D., Middendorf, M. (2004). Competition Controlled Pheromone Update for Ant Colony Optimization. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2004. Lecture Notes in Computer Science, vol 3172. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28646-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-28646-2_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22672-7
Online ISBN: 978-3-540-28646-2
eBook Packages: Springer Book Archive