Ant Colony Optimization with Immigrants Schemes in Dynamic Environments
In recent years, there has been a growing interest in addressing dynamic optimization problems (DOPs) using evolutionary algorithms (EAs). Several approaches have been developed for EAs to increase the diversity of the population and enhance the performance of the algorithm for DOPs. Among these approaches, immigrants schemes have been found beneficial for EAs for DOPs. In this paper, random, elitism-based, and hybrid immigrants schemes are applied to ant colony optimization (ACO) for the dynamic travelling salesman problem (DTSP). The experimental results show that random immigrants are beneficial for ACO in fast changing environments, whereas elitism-based immigrants are beneficial for ACO in slowly changing environments. The ACO algorithm with hybrid immigrants scheme combines the merits of the random and elitism-based immigrants schemes. Moreover, the results show that the proposed algorithms outperform compared approaches in almost all dynamic test cases and that immigrant schemes efficiently improve the performance of ACO algorithms in DTSP.
KeywordsAnt Colony Optimization Immigrants Schemes Dynamic Optimization
Unable to display preview. Download preview PDF.
- 4.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)Google Scholar
- 5.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)Google Scholar
- 7.Guntsch, M., Middendorf, M.: A population based approach for ACO. In: EvoWorkshops 2002: Appl. of Evol. Comput., pp. 72–81 (2002)Google Scholar
- 8.Guntsch, M., Middendorf, M.: Pheromone modification strategies for ant algorithms applied to dynamic TSP. In: EvoWorkshops 2001: Appl. of Evol. Comput., pp. 213–222 (2001)Google Scholar
- 9.Guntsch, M., Middendorf, M., Schmeck, H.: An ant colony optimization approach to dynamic TSP. In: Proc. of the 2001 Gen. and Evol. Comput. Conf., pp. 860–867 (2001)Google Scholar
- 10.Guo, T., Michalewicz, Z.: Inver-over operator for the TSP. In: Proc. of the 5th Int. Conf. on Parallel Problem Solving from Nature, pp. 803–812 (1998)Google Scholar
- 13.Stüzle, 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)Google Scholar
- 15.Yang, S.: Genetic algorithms with elitism based immigrants for changing optimization problems. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 627–636. Springer, Heidelberg (2007)Google Scholar
- 16.Yang, S.: Memory-based immigrants for genetic algorithms in dynamic environments. In: Proc. of the 2005 Genetic and Evol. Conf., vol. 2, pp. 1115–1122 (2005)Google Scholar