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Vehicle Routing and Adaptive Iterated Local Search within the HyFlex Hyper-heuristic Framework

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Learning and Intelligent Optimization (LION 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7219))

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Abstract

HyFlex (Hyper-heuristic Flexible framework) [15] is a software framework enabling the development of domain independent search heuristics (hyper-heuristics), and testing across multiple problem domains. This framework was used as a base for the first Cross-domain Heuristic Search Challenge, a research competition that attracted significant international attention. In this paper, we present one of the problems that was used as a hidden domain in the competition, namely, the capacitated vehicle routing problem with time windows. The domain implements a data structure and objective function for the vehicle routing problem, as well as many state-of- the-art low-level heuristics (search operators) of several types. The domain is tested using two adaptive variants of a multiple-neighborhood iterated local search algorithm that operate in a domain independent fashion, and therefore can be considered as hyper-heuristics. Our results confirm that adding adaptation mechanisms improve the performance of hyper-heuristics. It is our hope that this new and challenging problem domain can be used to promote research within hyper-heuristics, adaptive operator selection, adaptive multi-meme algorithms and autonomous control for search algorithms.

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References

  1. Baxter, J.: Local optima avoidance in depot location. Journal of the Operational Research Society 32, 815–819 (1981)

    Article  Google Scholar 

  2. Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E.: PISA – A Platform and Programming Language Independent Interface for Search Algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 494–508. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  3. Braysy, O., Gendreau, M.: Vehicle routing problem with time windows, part i: Route construction and local search algorithms. Transportation Science (2005)

    Google Scholar 

  4. Burke, E.K., Curtois, T., Hyde, M., Kendall, G., Ochoa, G., Petrovic, S., Vazquez-Rodriguez, J.A., Gendreau, M.: Iterated local search vs. hyper-heuristics: Towards general-purpose search algorithms. In: IEEE Congress on Evolutionary Computation (CEC 2010), Barcelona, Spain, pp. 3073–3080 (July 2010)

    Google Scholar 

  5. Burke, E.K., Gendreau, M., Hyde, M., Kendall, G., McCollum, B., Ochoa, G., Parkes, A.J., Petrovic, S.: The Cross-Domain Heuristic Search Challenge – An International Research Competition. In: Coello, C.A.C. (ed.) LION 5. LNCS, vol. 6683, pp. 631–634. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  6. Burke, E.K., Gendreau, M., Ochoa, G., Walker, J.D.: Adaptive iterated local search for cross-domain optimisation. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 1987–1994. ACM, New York (2011)

    Google Scholar 

  7. Burke, E.K., Hart, E., Kendall, G., Newall, J., Ross, P., Schulenburg, S.: Hyper-heuristics: An emerging direction in modern search technology. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 457–474. Kluwer (2003)

    Google Scholar 

  8. Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.: A Classification of Hyper-heuristic Approaches. In: Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 146, ch. 15, pp. 449–468. Springer (2010)

    Google Scholar 

  9. Cowling, P.I., Kendall, G., Soubeiga, E.: A Hyperheuristic Approach to Scheduling a Sales Summit. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 176–190. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  10. Fialho, Á., Da Costa, L., Schoenauer, M., Sebag, M.: Extreme Value Based Adaptive Operator Selection. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN X. LNCS, vol. 5199, pp. 175–184. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  11. Gendreau, M., Hertz, A., Laporte, G.: A new insertion and postoptimization procedures for the traveling salesman problem. Operations Research (1992)

    Google Scholar 

  12. Lourenco, H.R., Martin, O., Stutzle, T.: Iterated Local Search, pp. 321–353. Kluwer Academic Publishers, Dordrecht (2002)

    Google Scholar 

  13. Martin, O., Otto, S.W., Felten, E.W.: Large-step Markov chains for the TSP incorporating local search heuristics. Operations Research Letters 11(4), 219–224 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  14. Ochoa, G., Hyde, M.: The Cross-domain Heuristic Search Challenge, CHeSC 2011 (2011), http://www.asap.cs.nott.ac.uk/external/chesc2011/

  15. Ochoa, G., Hyde, M., Curtois, T., Vazquez-Rodriguez, J.A., Walker, J., Gendreau, M., Kendall, G., McCollum, B., Parkes, A.J., Petrovic, S., Burke, E.K.: HyFlex: A Benchmark Framework for Cross-Domain Heuristic Search. In: Hao, J.-K., Middendorf, M. (eds.) EvoCOP 2012. LNCS, vol. 7245, pp. 136–147. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  16. Or, I.: Traveling salesman-type combinatorial problems and their relation to the logistics of regional blood banking. PhD thesis, Northwestern University, Evanston, IL (1976)

    Google Scholar 

  17. Potvin, J.-Y., Rousseau, J.-M.: An exchange heuristic for routeing problems with time windows. The Journal of the Operational Research Society (1995)

    Google Scholar 

  18. Savelsbergh, M.W.P.: The vehicle routing problem with time windows: Minimizing route duration. Informs Journal on Computing 4(2), 146–154 (1992)

    Article  MATH  Google Scholar 

  19. Schrimpf, G., Schneider, J., Stamm-Wilbrandt, H., Dueck, G.: Record breaking optimization results using the ruin and recreate principle. Journal of Computational Physics (2000)

    Google Scholar 

  20. SINTEF. VRPTW benchmark problems, on the SINTEF transport optimisation portal (2011), http://www.sintef.no/Projectweb/TOP/Problems/VRPTW/

  21. Taillard, E.: Benchmarks for basic scheduling problems. European Journal of Operational Research 64(2), 278–285 (1993)

    Article  MATH  Google Scholar 

  22. Thierens, D.: An adaptive pursuit strategy for allocating operator probabilities. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, GECCO 2005, pp. 1539–1546. ACM, New York (2005)

    Google Scholar 

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Walker, J.D., Ochoa, G., Gendreau, M., Burke, E.K. (2012). Vehicle Routing and Adaptive Iterated Local Search within the HyFlex Hyper-heuristic Framework. In: Hamadi, Y., Schoenauer, M. (eds) Learning and Intelligent Optimization. LION 2012. Lecture Notes in Computer Science, vol 7219. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_19

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  • DOI: https://doi.org/10.1007/978-3-642-34413-8_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34412-1

  • Online ISBN: 978-3-642-34413-8

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