Abstract
Ant colony optimization (ACO) algorithms have proved to be able to adapt to dynamic optimization problems (DOPs) when stagnation behaviour is avoided. Several approaches have been integrated with ACO to improve its performance for DOPs. The adaptation capabilities of ACO rely on the pheromone evaporation mechanism, where the rate is usually fixed. Pheromone evaporation may eliminate pheromone trails that represent bad solutions from previous environments. In this paper, an adaptive scheme is proposed to vary the evaporation rate in different periods of the optimization process. The experimental results show that ACO with an adaptive pheromone evaporation rate achieves promising results, when compared with an ACO with a fixed pheromone evaporation rate, for different DOPs.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
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.
References
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. on Syst., Man and Cybern., Part B: Cybern. 26(1), 29–41 (1996)
Dorigo, M., Stützle, T.: Ant colony optimization. The MIT Press, London (2004)
Eyckelhof, C.J., Snoek, M.: Ant Systems for a Dynamic TSP: Ants Caught in a Traffic Jam. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) ANTS 2002. LNCS, vol. 2463, pp. 88–99. Springer, Heidelberg (2002)
Gambardella, M.L., Dorigo, M.: Ant-Q: A reinforcement learning approach to the traveling salesman problem. In: Proc of the 12th Int. Conf. on Machine Learning, pp. 252–260. Morgan Kaufmann (1995)
Guntsch, M., Middendorf, M.: Applying Population Based ACO to Dynamic Optimization Problems. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) ANTS 2002. LNCS, vol. 2463, pp. 111–122. Springer, Heidelberg (2002)
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.) EvoWorkshop 2001. LNCS, vol. 2037, pp. 213–222. Springer, Heidelberg (2001)
Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments - a survey. IEEE Trans. on Evol. 9(3), 303–317 (2005)
Mavrovouniotis, M., Yang, S.: Ant colony optimization with memory-based immigrants for the dynamic vehicle routing problem. In: Proc. of the 2012 IEEE Congress on Evol. Comput., pp. 2645–2652. IEEE Press (2012)
Mavrovouniotis, M., Yang, S., Yao, X.: A Benchmark Generator for Dynamic Permutation-Encoded Problems. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part II. LNCS, vol. 7492, pp. 508–517. Springer, Heidelberg (2012)
Pellegrini, P., Stützle, T., Birattari, M.: A critical analysis of parameter adaptation in ant colony optimization. Swarm Intelli. 6(1), 23–48 (2012)
Stützle, T., Hoos, H.: The \(\mathcal{MAX}\)-\(\mathcal{MIN}\) Ant System and local search for the traveling salesman problem. In: Proc. of the 1997 IEEE Int. Conf. on Evol. Comput., pp. 309–314. IEEE Press (1997)
Stützle, T., López-Ibáñez, M., Pellegrini, P., Maur, M., de Oca, M.M., Birattari, M., Dorigo, M.: Parameter adaptation in ant colony optimization, pp. 191–215. Springer, Heidelberg (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mavrovouniotis, M., Yang, S. (2013). Adapting the Pheromone Evaporation Rate in Dynamic Routing Problems. In: Esparcia-Alcázar, A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37192-9_61
Download citation
DOI: https://doi.org/10.1007/978-3-642-37192-9_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37191-2
Online ISBN: 978-3-642-37192-9
eBook Packages: Computer ScienceComputer Science (R0)