Abstract
Ant colony optimization (ACO) algorithms have proved to be able to adapt to dynamic optimization problems (DOPs) when stagnation behaviour is addressed. Usually, permutation-encoded DOPs, e.g., dynamic travelling salesman problems, are addressed using ACO algorithms whereas binary-encoded DOPs, e.g., dynamic knapsack problems, are tackled by evolutionary algorithms (EAs). This is because of the initial developments of the introduced to address binary-encoded DOPs and compared with existing EAs. The experimental results show that ACO with an appropriate pheromone evaporation rate outperforms EAs in most dynamic test cases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dorigo, M., Stützle, T. (eds.): Ant Colony Optimization. MIT Press, London (2004)
Alaya, I., Solnon, C., Ghédira, K.: Ant algorithm for the multi-dimensional knapsack problem. In: International Conference on Bioinspired Optimization Methods and their Applications, pp. 63–72 (2004)
Ke, L., Feng, Z., Ren, Z., Wei, X.: An ant colony optimization approach for the multidimensional knapsack problem. J. Heuristics 16(1), 65–83 (2010)
Kong, M., Tian, P.: Introducing a binary ant colony optimization. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 444–451. Springer, Heidelberg (2006)
Kong, M., Tian, P., Kao, Y.: A new ant colony optimization algorithm for the multidimensional knapsack problem. Comput. Oper. Res. 35(8), 2672–2683 (2008)
Leguizamon, G., Michalewicz, Z.: A new version of ant system for subset problems. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, vol. 2, pp. 1459–1464 (1999)
Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments - a survey. IEEE Trans. Evol. Comput. 9(3), 303–317 (2005)
Nguyen, T.T., Yang, S., Branke, J.: Evolutionary dynamic optimization: A survey of the state of the art. Swarm Evol. Comput. 6, 1–24 (2012)
Angus, D., Hendtlass, T.: Ant colony optimisation applied to a dynamically changing problem. In: Hendtlass, T., Ali, M. (eds.) IEA/AIE 2002. LNCS (LNAI), vol. 2358, pp. 618–627. Springer, Heidelberg (2002)
Mavrovouniotis, M., Yang, S.: Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors. Appl. Soft Comput. 13(10), 4023–4037 (2013)
Guntsch, M., Middendorf, M., Schmeck, H.: An ant colony optimization approach to dynamic tsp. In: Proceedings of the 2001 Genetic and Evolutionary Computation Conference, pp. 860–867 (2001)
Mavrovouniotis, M., Yang, S.: Ant colony optimization with immigrants schemes for the dynamic vehicle routing problem. In: Di Chio, C., Agapitos, A., Cagnoni, S., Cotta, C., de Vega, F.F., Di Caro, G.A., Drechsler, R., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Langdon, W.B., Merelo-Guervós, J.J., Preuss, M., Richter, H., Silva, S., Simões, A., Squillero, G., Tarantino, E., Tettamanzi, A.G.B., Togelius, J., Urquhart, N., Uyar, A.Ş., Yannakakis, G.N. (eds.) EvoApplications 2012. LNCS, vol. 7248, pp. 519–528. Springer, Heidelberg (2012)
Montemanni, R., Gambardella, L.M., Rizzoli, A.E., Donati, A.V.: Ant colony system for a dynamic vehicle routing problem. Comb. Optim. 10, 327–343 (2005)
Yang, S.: Genetic algorithms with memory- and elitism-based immigrants in dynamic environments. Evol. Comput. 16(3), 385–416 (2008)
Yang, S., Yao, X.: Population-based incremental learning with associative memory for dynamic environments. IEEE Trans. Evol. Comput. 12(5), 542–561 (2008)
Fernandes, C.M., Rosa, A.C., Ramos, V.: Binary ant algorithm. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, GECCO 2007, pp. 41–48. ACM, New York (2007)
Yang, S.: Non-stationary problem optimization using the primal-dual genetic algorithm. In: Proceedings of the 2003 IEEE Congress on Evolutionary Computation, pp. 2246–2253 (2003)
Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of the European Conference on Artificial Life, pp. 134–142. Elsevier (1991)
Stützle, T., Hoos, H.: The max-min ant system and local search for the traveling salesman problem. In: Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, pp. 309–314 (1997)
Fidanova, S.: Aco algorithm for MKP using various heuristic information. In: Dimov, I., Lirkov, I., Margenov, S., Zlatev, Z. (eds.) NMA 2002. LNCS 2542, pp. 438–444. Springer, Berlin Heidelberg (2003)
Acknowledgement
This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of U.K. under Grant EP/K001310/1.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Mavrovouniotis, M., Yang, S. (2015). Applying Ant Colony Optimization to Dynamic Binary-Encoded Problems. In: Mora, A., Squillero, G. (eds) Applications of Evolutionary Computation. EvoApplications 2015. Lecture Notes in Computer Science(), vol 9028. Springer, Cham. https://doi.org/10.1007/978-3-319-16549-3_68
Download citation
DOI: https://doi.org/10.1007/978-3-319-16549-3_68
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16548-6
Online ISBN: 978-3-319-16549-3
eBook Packages: Computer ScienceComputer Science (R0)