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An Immigrants Scheme Based on Environmental Information for Ant Colony Optimization for the Dynamic Travelling Salesman Problem

  • Michalis Mavrovouniotis
  • Shengxiang Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7401)

Abstract

Ant colony optimization (ACO) algorithms have proved to be powerful methods to address dynamic optimization problems. However, once the population converges to a solution and a dynamic change occurs, it is difficult for the population to adapt to the new environment since high levels of pheromone will be generated to a single trail and force the ants to follow it even after a dynamic change. A good solution is to maintain the diversity via transferring knowledge to the pheromone trails. Hence, we propose an immigrants scheme based on environmental information for ACO to address the dynamic travelling salesman problem (DTSP) with traffic factor. The immigrants are generated using a probabilistic distribution based on the frequency of cities, constructed from a number of ants of the previous iteration, and replace the worst ants in the current population. Experimental results based on different DTSP test cases show that the proposed immigrants scheme enhances the performance of ACO by the knowledge transferred from the previous environment and the generation of guided diversity.

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References

  1. 1.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm intelligence: from natural to artificial systems. Oxford University Press, New York (1999)zbMATHGoogle Scholar
  2. 2.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst., Man and Cybern., Part B: Cybern. 26(1), 29–41 (1996)CrossRefGoogle Scholar
  3. 3.
    Eyckelhof, C.J., Snoek, M.: Ant Systems for a Dynamic TSP. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, p. 88. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Grefenestette, J.J.: Genetic algorithms for changing environments. In: Proc. 2nd Int. Conf. on Parallel Problem Solving from Nature, pp. 137–144 (1992)Google Scholar
  5. 5.
    Guntsch, M., Middendorf, M.: Applying Population Based ACO to Dynamic Optimization Problems. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 111–122. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Guntsch, M., Middendorf, M.: Pheromone Modification Strategies for Ant Algorithms Applied to Dynamic TSP. In: Boers, E.J.W., Gottlieb, J., Lanzi, P.L., Smith, R.E., Cagnoni, S., Hart, E., Raidl, G.R., Tijink, H. (eds.) EvoWorkshops 2001. LNCS, vol. 2037, pp. 213–222. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  7. 7.
    Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments - a survey. IEEE Trans. Evol. Comput. 9(3), 303–317 (2005)CrossRefGoogle Scholar
  8. 8.
    Mavrovouniotis, M., Yang, S.: Ant Colony Optimization with Immigrants Schemes in Dynamic Environments. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6239, pp. 371–380. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Mavrovouniotis, M., Yang, S.: Memory-Based Immigrants for Ant Colony Optimization in Changing Environments. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcázar, A.I., Merelo, J.J., Neri, F., Preuss, M., Richter, H., Togelius, J., Yannakakis, G.N. (eds.) EvoApplications 2011, Part I. LNCS, vol. 6624, pp. 324–333. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Neumann, F., Witt, C.: Runtime analysis of a simple ant colony optimization algorithm. Algorithmica 54(2), 243–255 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Rizzoli, A.E., Montemanni, R., Lucibello, E., Gambardella, L.M.: Ant colony optimization for real-world vehicle routing problems – from theory to applications. Swarm Intelli. 1(2), 135–151 (2007)CrossRefGoogle Scholar
  12. 12.
    Stützle, T., Hoos, H.: The MAX-MIN ant system and local search for the traveling salesman problem. In: Proc. 1997 IEEE Int. Conf. on Evol. Comput., pp. 309–314 (1997)Google Scholar
  13. 13.
    Yang, S.: Genetic algorithms with memory and elitism based immigrants in dynamic environments. Evol. Comput. 16(3), 385–416 (2008)CrossRefGoogle Scholar
  14. 14.
    Yu, X., Tang, K., Yao, X.: An immigrants scheme based on environmental information for genetic algorithms in changing environments. In: Proc. 2008 IEEE Cong. of Evol. Comput., pp. 1141–1147 (2008)Google Scholar
  15. 15.
    Yu, X., Tang, K., Chen, T., Yao, X.: Empirical analysis of evolutionary algorithms with immigrants schemes for dynamic optimization. Memetic Comput. 1(1), 3–24 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Michalis Mavrovouniotis
    • 1
  • Shengxiang Yang
    • 2
  1. 1.Department of Computer ScienceUniversity of LeicesterLeicesterUnited Kingdom
  2. 2.Department of Information Systems and ComputingBrunel UniversityUxbridgeUnited Kingdom

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