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Population-based vs. Single-solution Heuristics for the Travelling Thief Problem

Published:20 July 2016Publication History

ABSTRACT

The Travelling Thief Problem (TTP) is an optimization problem introduced in order to provide a more realistic model for real-world optimization problems. The problem combines the Travelling Salesman Problem and the Knapsack Problem and introduces the notion of interdependence between sub-problems. In this paper, we study and compare different approaches for solving the TTP from a metaheuristics perspective. Two heuristic algorithms are proposed. The first is a Memetic Algorithm, and the second is a single-solution heuristic empowered by Hill Climbing and Simulated Annealing. Two other state-of-the-art algorithms are briefly revisited, analyzed, and compared to our algorithms. The obtained results prove that our algorithms are very efficient for many TTP instances.

References

  1. D. Applegate, W. Cook, and A. Rohe. Chained lin-kernighan for large traveling salesman problems. INFORMS Journal on Computing, 15(1):82--92, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. A. Beham, J. Fechter, M. Kommenda, S. Wagner, S. M. Winkler, and M. Affenzeller. Optimization strategies for integrated knapsack and traveling salesman problems. In Computer Aided Systems Theory--EUROCAST 2015, pages 359--366. Springer, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  3. M. R. Bonyadi, Z. Michalewicz, and L. Barone. The travelling thief problem: the first step in the transition from theoretical problems to realistic problems. In Evolutionary Computation (CEC), 2013 IEEE Congress on, pages 1037--1044. IEEE, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  4. M. R. Bonyadi, Z. Michalewicz, M. Roman Przybyoek, and A. Wierzbicki. Socially inspired algorithms for the travelling thief problem. In Proceedings of the 2014 conference on Genetic and evolutionary computation, pages 421--428. ACM, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. B. Delaunay. Sur la sphere vide. Izv. Akad. Nauk SSSR, Otdelenie Matematicheskii i Estestvennyka Nauk, 7(793--800):1--2, 1934.Google ScholarGoogle Scholar
  6. J. Dzubera and D. Whitley. Advanced correlation analysis of operators for the traveling salesman problem. In Parallel Problem Solving from Nature-PPSN III, pages 68--77. Springer, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. H. Faulkner, S. Polyakovskiy, T. Schultz, and M. Wagner. Approximate approaches to the traveling thief problem. In Proceedings of the 2015 on Genetic and Evolutionary Computation Conference, pages 385--392. ACM, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. D. E. Goldberg and R. Lingle. Alleles, loci, and the traveling salesman problem. In Proceedings of the first international conference on genetic algorithms and their applications, pages 154--159. Lawrence Erlbaum Associates, Publishers, 1985. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. L. Guibas and J. Stolfi. Primitives for the manipulation of general subdivisions and the computation of voronoi. ACM transactions on graphics (TOG), 4(2):74--123, 1985. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. J.-Q. Hu and B. Leida. Traffic grooming, routing, and wavelength assignment in optical wdm mesh networks. In INFOCOM 2004. Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies, volume 1. IEEE, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  11. M. Ibrahimov, A. Mohais, S. Schellenberg, and Z. Michalewicz. Evolutionary approaches for supply chain optimisation: part i: single and two-component supply chains. International Journal of Intelligent Computing and Cybernetics, 5(4):444--472, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  12. M. Ibrahimov, A. Mohais, S. Schellenberg, and Z. Michalewicz. Evolutionary approaches for supply chain optimisation. part ii: multi-silo supply chains. International Journal of Intelligent Computing and Cybernetics, 5(4):473--499, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  13. M. Iori and S. Martello. Routing problems with loading constraints. Top, 18(1):4--27, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  14. S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi, et al. Optimization by simulated annealing. science, 220(4598):671--680, 1983.Google ScholarGoogle Scholar
  15. Y. Mei, X. Li, F. Salim, and X. Yao. Heuristic evolution with genetic programming for traveling thief problem. In Evolutionary Computation (CEC), 2015 IEEE Congress on, pages 2753--2760. IEEE, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  16. Y. Mei, X. Li, and X. Yao. Improving efficiency of heuristics for the large scale traveling thief problem. In Simulated Evolution and Learning, pages 631--643. Springer, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Y. Mei, X. Li, and X. Yao. On investigation of interdependence between sub-problems of the travelling thief problem. Soft Computing, pages 1--16, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Z. Michalewicz and D. B. Fogel. How to solve it: modern heuristics. Springer Science & Business Media, 2013.Google ScholarGoogle Scholar
  19. H. Muhlenbein. Evolution in time and space-the parallel genetic algorithm. In Foundations of genetic algorithms. Citeseer, 1991.Google ScholarGoogle Scholar
  20. J. Perl and M. S. Daskin. A warehouse location-routing problem. Transportation Research Part B: Methodological, 19(5):381--396, 1985.Google ScholarGoogle ScholarCross RefCross Ref
  21. S. Polyakovskiy, M. R. Bonyadi, M. Wagner, Z. Michalewicz, and F. Neumann. A comprehensive benchmark set and heuristics for the traveling thief problem. In Proceedings of the 2014 conference on Genetic and evolutionary computation, pages 477--484. ACM, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. D. Whitley, D. Hains, and A. Howe. Tunneling between optima: partition crossover for the traveling salesman problem. In Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pages 915--922. ACM, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. D. Whitley, D. Hains, and A. Howe. A hybrid genetic algorithm for the traveling salesman problem using generalized partition crossover. In Parallel Problem Solving from Nature, PPSN XI, pages 566--575. Springer, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. D. Whitley, T. Starkweather, and D. Shaner. The traveling salesman and sequence scheduling: Quality solutions using genetic edge recombination. Citeseer, 1991.Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Conferences
      GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016
      July 2016
      1196 pages
      ISBN:9781450342063
      DOI:10.1145/2908812

      Copyright © 2016 ACM

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      Publication History

      • Published: 20 July 2016

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      GECCO '16 Paper Acceptance Rate137of381submissions,36%Overall Acceptance Rate1,669of4,410submissions,38%

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