Abstract
The CHC algorithm uses an elitist selection method which, combined with an incest prevention mechanism and a method to diverge the population whenever it converges, allows the maintenance of the population diversity. This algorithm was successfully used in the past for static optimization problems. In this paper we propose three new and improved CHC-based algorithms designed to deal with dynamic environments. The performance of the investigated CHC algorithms is tested in different instances of the dynamic Traveling Salesman Problem. The experimental results show the efficiency, robustness and adaptability of the improved CHC variants solving different dynamic traveling salesman problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bosman, P.A.N.: Learning and anticipation in online dynamic optimization. In: Yang, S., Ong, Y., Jin, Y. (eds.) Evolutionary Computation in Dynamic and Uncertain Environments. Springer, Heidelberg (2007)
Branke, J., Kaußler, T., Schmidt, C.: A multi-population approach to dynamic optimization problems. In: Parmee, I. (ed.) Proceedings of Adaptive Computing in Design and Manufacture (ACDM 2000), pp. 299–308. Springer, Heidelberg (2000)
Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Dordrecht (2002)
Li, C., Yang, M., Kang, L.: A new approach to solving dynamic traveling salesman problems. In: Wang, T.D., et al. (eds.) SEAL 2006. LNCS, vol. 4247, pp. 236–243. Springer, Heidelberg (2006)
Cobb, H.G.: An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Tech. Rep. TR AIC-90-001, Naval Research Laboratory (1990)
Davis, L.: Job shop scheduling with genetic algorithms. In: Proceedings of the 1st International Conference on Genetic Algorithms, pp. 136–140. L. Erlbaum Associates Inc., Mahwah (1985)
Eshelman, L.J.: The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In: Rawlins, G.J.E. (ed.) Foundations of Genetic Algorithms I, pp. 265–283. Morgan Kaufmann, San Francisco (1991)
Grefenstette, J.J.: Genetic algorithms for changing environments. In: Männer, R., Manderick, B. (eds.) Proceedings of Parallel Problem Solving from Nature (PPSN II), pp. 137–144 (1992)
Guntsch, M., Middendorf, M.: A population based approach for ACO. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds.) EvoIASP 2002, EvoWorkshops 2002, EvoSTIM 2002, EvoCOP 2002, and EvoPlan 2002. LNCS, vol. 2279, pp. 72–81. Springer, Heidelberg (2002)
Liu, L., Wang, D., Yang, S.: An immune system based genetic algorithm using permutation-based dualism for dynamic traveling salesman problems. In: Giacobini, M., et al. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 725–734. Springer, Heidelberg (2009)
Psaraftis, H.N.: Dynamic vehicle routing problems. In: Golden, B.L., Assad, A.A. (eds.) Vehicle Routing: Methods and Studies, pp. 223–248. Elsevier, Amsterdam (1988)
Reinelt, G.: Tsplib. university of heidelberg (1995), http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/
Simões, A., Costa, E.: Variable-size memory evolutionary algorithm to deal with dynamic environments. In: Giacobini, M., et al. (eds.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 617–626. Springer, Heidelberg (2007)
Simões, A., Costa, E.: Prediction in evolutionary algorithms for dynamic environments using markov chains and nonlinear regression. In: Proceedings of the 11th International Genetic and Evolutionary Computation Conference (GECCO 2009), pp. 883–890. ACM Press, New York (2009)
Wang, D., Liu, S.: An agent-based evolutionary search for dynamic traveling salesman problem. In: International Conference on Information Engineering, vol. 1, pp. 111–114 (2010)
Whitley, D., Rana, S., Dzubera, J., Mathias, E.: Evaluating evolutionary algorithms. Arificial Intelligence (85), 245–276 (1996)
Yan, X.S., Liu, H.M., Yan, J., Hua Wu, Q.: A fast evolutionary algorithm for traveling salesman problem. In: Proceedings of the 3rd International Conference on Natural Computation (ICNC 2007), vol. 4, pp. 85–90. IEEE, Los Alamitos (2007)
Yang, S.: Explicit memory schemes for evolutionary algorithms in dynamic environments. In: Yang, S., Ong, Y.S., Jin, Y. (eds.) Evolutionary Computation in Dynamic and Uncertain Environments, pp. 3–28. Springer, Heidelberg (2007)
Yang, S.: GAs with elitism-based immigrants for changing optimization problems. In: Giacobini, M., et al. (eds.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 627–636. Springer, Heidelberg (2007)
Younes, A., Basir, O., Calamai, P.: A benchmark generator for dynamic optimization. In: Proceedings of the 3rd International Conference on Soft Computing, Optimization, Simulation & Manufacturing Systems, WSEAS 2003 (2003)
Zhou, A., Kang, L., Yan, Z.: Solving dynamic tsp with evolutionary approach in real time. In: Proceedings of the 2003 IEEE Congress on Evolutionary Computation (CEC 2003), pp. 951–957. IEEE, Los Alamitos (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Simões, A., Costa, E. (2011). CHC-Based Algorithms for the Dynamic Traveling Salesman Problem. 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_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-20525-5_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20524-8
Online ISBN: 978-3-642-20525-5
eBook Packages: Computer ScienceComputer Science (R0)