Abstract
Ant colony optimization (ACO) algorithms have proved that they can adapt to dynamic optimization problems (DOPs) when they are enhanced to maintain diversity. DOPs are important due to their similarities to many real-world applications. Several approaches have been integrated with ACO to improve their performance in DOPs, where memory-based approaches and immigrants schemes have shown good results on different variations of the dynamic travelling salesman problem (DTSP). In this paper, we consider a novel variation of DTSP where traffic jams occur in a cyclic pattern. This means that old environments will re-appear in the future. A hybrid method that combines memory and immigrants schemes is proposed into ACO to address this kind of DTSPs. The memory-based approach is useful to directly move the population to promising areas in the new environment by using solutions stored in the memory. The immigrants scheme is useful to maintain the diversity within the population. The experimental results based on different test cases of the DTSP show that the memory-based immigrants scheme enhances the performance of ACO in cyclic dynamic environments.
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References
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)
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. In: ANTS 2002: Proc. of the 3rd Int. Workshop on Ant Algorithms, pp. 88–99 (2002)
Grefenestette, J.J.: Genetic algorithms for changing environments. In: Proc. of the 2nd Int. Conf. on Parallel Problem Solving from Nature, pp. 137–144 (1992)
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)
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.) EvoIASP 2001, EvoWorkshops 2001, EvoFlight 2001, EvoSTIM 2001, EvoCOP 2001, and EvoLearn 2001. LNCS, vol. 2037, pp. 213–222. Springer, Heidelberg (2001)
Guntsch, M., Middendorf, M., Schmeck, H.: An ant colony optimization approach to dynamic TSP. In: Proc. of the 2001 Genetic and Evol. Comput. Conf., pp. 860–867 (2001)
Guo, T., Michalewicz, Z.: Inver-over operator for the TSP. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 803–812. Springer, Heidelberg (1998)
Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments - a survey. IEEE Trans. on Evol. Comput. 9(3), 303–317 (2005)
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)
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)
Stützle, T., Hoos, H.: The MAX-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 (1997)
Yang, S.: Memory-based immigrants for genetic algorithms in dynamic environments. In: Proc. of the 2005 Genetic and Evol. Comput. Conf., vol. 2, pp. 1115–1122 (2005)
Yang, S.: Genetic algorithms with memory and elitism based immigrants in dynamic environments. Evol. Comput. 16(3), 385–416 (2008)
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Mavrovouniotis, M., Yang, S. (2011). Memory-Based Immigrants for Ant Colony Optimization in Changing Environments. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6624. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20525-5_33
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DOI: https://doi.org/10.1007/978-3-642-20525-5_33
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