Abstract
Memetic algorithms usually employ long running times, since local search is performed every time a new solution is generated. Acceleration of a memetic algorithm requires focusing on local search, the most time-consuming component. This paper describes the application of two acceleration techniques to local search in a memetic algorithm: caching of values of objective function for neighbours and forbidding moves which could increase distance between solutions. Computational experiments indicate that in the capacitated vehicle routing problem the usage of these techniques is not really profitable, because of cache management overhead and implementation issues.
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References
Merz, P.: Advanced fitness landscape analysis and the performance of memetic algorithms. Evolutionary Computation 12(3), 303–325 (2004)
Jaszkiewicz, A.: Genetic local search for multiple-objective combinatorial optimization. European Journal of Operational Research 137(1), 50–71 (2002)
Ishibuchi, H., Yoshida, T., Murata, T.: Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Transactions on Evolutionary Computation 7(2), 204–223 (2003)
Bentley, J.L.: Experiments on traveling salesman heuristics. In: Proceedings of the first annual ACM-SIAM symposium on discrete algorithms, pp. 91–99 (1990)
Jaszkiewicz, A., Kominek, P.: Genetic local search with distance preserving recombination operator for a vehicle routing problem. European Journal of Operational Research 151(2), 352–364 (2003)
Reeves, C.R.: Landscapes, operators and heuristic search. Annals of Operations Research 86(1), 473–490 (1999)
Kubiak, M.: Systematic construction of recombination operators for the vehicle routing problem. Foundations of Computing and Decision Sciences 29(3), 205–226 (2004)
Merz, P., Freisleben, B.: A genetic local search approach to the quadratic assign- ment problem. In: Bäck, T. (ed) Proceedings of the Seventh International Conference on Genetic Algorithms (1997)
Jaszkiewicz, A.: Improving performance of genetic local search by changing local search space topology. Foundations of Computing and Decision Sciences 24(2), 77–84 (1999)
Toth, P., Vigo, D.: The Vehicle Routing Problem. SIAM, Philadelphia (2002)
Hoos, H.H., Stutzle, T.: Stochastic Local Search: Foundations and Applications. Morgan Kauffman, Washington (2004)
Kindervater, G.A.P., Savelsbergh, M.W.P.: Vehicle routing: handling edge exchanges. In: Aarts, E., Lenstra, J.K. (eds.) Local Search in Combinatorial Optimization, pp. 337–360. John Wiley & Sons, New York (1997)
Prins, C.: A simple and effective evolutionary algorithm for the vehicle routing problem. In: de Sousa, J.P., (ed) Proceedings of the 4th Metaheuristics International Conference, MIC 2001, pp. 143–147 (2001)
Rochat, Y., Taillard, É.D.: Probabilistic diversification and intensification in local search for vehicle routing. Journal of Heuristics 1(1), 147–167 (1995)
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Kubiak, M., Wesołek, P. (2007). Accelerating Local Search in a Memetic Algorithm for the Capacitated Vehicle Routing Problem. In: Cotta, C., van Hemert, J. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2007. Lecture Notes in Computer Science, vol 4446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71615-0_9
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DOI: https://doi.org/10.1007/978-3-540-71615-0_9
Publisher Name: Springer, Berlin, Heidelberg
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