Sensitive Ants: Inducing Diversity in the Colony

  • C. -M. Pintea
  • C. Chira
  • D. Dumitrescu
Part of the Studies in Computational Intelligence book series (SCI, volume 236)


A metaheuristic called Sensitive Ant Model (SAM) for solving combinatorial optimization problems is developed. The proposed model is based on the Ant Colony System (ACS) algorithm in which agents cooperate indirectly using pheromone trails. SAM improves and extends the ACS approach by endowing each agent of the model with properties that induce heterogeneity. Different pheromone sensitivity levels are used for agents in the population. Highly-sensitive agents are essentially influenced in the decision making process by stigmergic information and thus likely to select strong pheromone-marked moves. Search intensification can be therefore sustained. Agents with low sensitivity are biased towards random search inducing diversity for exploration of the environment. A balanced sensitivity distribution is facilitated by the co-existence of several subpopulations with pheromone sensitivity level generated in a specified range. A heterogeneous agent model has the potential to cope with complex search spaces. Sensitive agents (or ants) allow many types of reactions to a changing environment facilitating an efficient balance between exploration and exploitation.


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  1. 1.
    Chira, C., Dumitrescu, D., Pintea, C.-M.: Heterogeneous Sensitive Ant Model for Combinatorial Optimization. In: Genetic and Evolutionary Computation Conference GECCO 2008, Atlanta, Georgia, USA, July 12-16, 2008, pp. 163–164. ACM, New York (2008)CrossRefGoogle Scholar
  2. 2.
    Chira, C., Dumitrescu, D., Pintea, C.-M.: Sensitive Ant Model for Combinatorial Optimization. In: Radomil, M. (ed.) 14th International Conference in Soft Computing MENDEL, Brno University of Technology (2008)Google Scholar
  3. 3.
    Crainic, T.G., Toulouse, M.: Parallel Strategies for Metaheuristics. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 475–513. Kluwer Academic Publishers, Dordrecht (2002)Google Scholar
  4. 4.
    Dorigo, M., Gambardella, L.M.: Ant Colony System: A cooperative learning approach to the traveling salesman problem. IEEE Trans. on Systems Man and Cybernetics 26, 29–41 (1996)CrossRefGoogle Scholar
  5. 5.
    Dorigo, M., Stützle, T.: The Ant Colony Optimization Metaheuristic: Algorithms, Applications and Advances. In: Handbook of Metaheuristics (2002)Google Scholar
  6. 6.
    Grassé, P.P.: La Reconstruction du nid et les Coordinations Inter-Individuelles chez Bellicositermes Natalensis et Cubitermes sp. La theorie de la Stigmergie: Essai d’interpretation du Comportement des Termites Constructeurs. Insectes Sociaux 6, 41–81 (1959)CrossRefGoogle Scholar
  7. 7.
    Helsgaun, K.: An effective implementation of the linkernighan TSP heuristic. European J. of Oper. Res. 126, 106–130 (2000)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Kaveh, A., Shahrouzi, M.: A hybrid ant strategy and genetic algorithm to tune the population size for efficient structural optimization. Engineering Computations 24(3), 237–254 (2007)CrossRefGoogle Scholar
  9. 9.
    Nakamichi, Y., Arita, T.: Diversity control in ant colony optimization. Artificial Life and Robotics, Springer Japan 7(4), 198–204 (2004)CrossRefGoogle Scholar
  10. 10.
    Stützle, T., Hoos, H.H.: Max-Min Ant System. Future Generation Comp Systems 16(9), 889–914 (2000)CrossRefGoogle Scholar
  11. 11.
  12. 12.
  13. 13.
  14. 14.

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • C. -M. Pintea
    • 1
  • C. Chira
    • 1
  • D. Dumitrescu
    • 1
  1. 1.Babes-Bolyai UniversityCluj-NapocaRomania

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