Abstract
In this paper, we are interested in a recent variant of the Traveling Salesman Problem (TSP) in which the Knapsack Problem (KP) is integrated. In the literature, this problem is called Traveling Thief Problem (TTP). Interested by the inherent computational challenge of the new problem, we investigate a hybrid evolutionary approach for solving the TTP. In order to evaluate the efficiency of the proposed approach, we carried out some numerical experiments on benchmark instances. The results show the efficiency of the introduced method in solving medium sized instances. In particular, the algorithm improves some best known solutions of the literature. In the case of large instances, our approach gives competitive results that need to be improved in order to obtain better results.
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
Notes
- 1.
TSP Test Data, see comopt.ifi.uni-heidelberg.de/software/TSPLIB95/index.html.
- 2.
Wagner et al. have done their computational experiments on an Xeon 2.66 GHz quad core CPU and ran each algorithm for 10 min [21].
- 3.
Solutions available at comopt.ifi.uni-heidelberg.de/software/TSPLIB95/STSP.html.
References
Blickle, T., Thiele, L.: A comparison of selection schemes used in evolutionary algorithms. Evol. Comput. 4(4), 361–394 (1996)
Bonyadi, M.R., Michalewicz, Z., Barone, L.: The travelling thief problem: the first step in the transition from theoretical problems to realistic problems. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1037–1044 (2013)
Bonyadi, M.R., Michalewicz, Z., Przybyoek, M.R., Wierzbicki, A.: Socially inspired algorithms for the travelling thief problem. In: GECCO 2014, pp. 421–428 (2014)
Chand, S., Wagner, M.: Fast heuristics for the multiple traveling thieves problem In: GECCO 2016, pp. 293–300 (2016)
El Yafrani, M., Ahiod, B.: Population-based vs. single-solution heuristics for the travelling thief problem. In: GECCO 2016, pp. 317–324 (2016)
Faulkner, H., Polyakovskiy, S., Schultz, T., Wagner, M.: Approximate approaches to the traveling thief problem. In: GECCO 2015, pp. 385–392 (2015)
Floudas, C.A., Pardalos, P.M. (eds.): Encyclopedia of Optimization. Springer, New York (2009)
Gendreau, M., Potvin, J.-Y.: Handbook of Metaheuristics. Springer, New York (2010)
Goeke, D., Moeini, M., Poganiuch, D.: A variable neighborhood search heuristic for the maximum ratio clique problem. Comput. Oper. Res. 1–9 (2017, forthcoming). doi:10.1016/j.cor.2017.01.010
Gupta, B.C., Prakash, V.P.: Greedy heuristics for the travelling thief problem. In: 39th National Systems Conference (NSC), pp. 1–5 (2015)
Gueye, S., Michel, S., Moeini, M.: Adjacency variables formulation for the minimum linear arrangement problem. In: Pinson, E., Valente, F., Vitoriano, B. (eds.) ICORES 2014. CCIS, vol. 509, pp. 95–107. Springer, Cham (2015). doi:10.1007/978-3-319-17509-6_7
Mei, Y., Li, X., Yao, X.: Improving efficiency of heuristics for the large scale traveling thief problem. In: Dick, G., et al. (eds.) SEAL 2014. LNCS, vol. 8886, pp. 631–643. Springer, Cham (2014). doi:10.1007/978-3-319-13563-2_53
Mei, Y., Li, X., Salim, F., Yao, X.: Heuristic evolution with genetic programming for traveling thief problem. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2753–2760 (2015)
Mei, Y., Li, X., Yao, X.: On investigation of interdependence between sub-problems of the travelling thief problem. Soft. Comput. 20(1), 157–172 (2016)
Lourenço, N., Pereira, F.B., Costa, E.: An evolutionary approach to the full optimization of the traveling thief problem. In: EvoCOP, pp. 34–45 (2016)
Polyakovskiy, S., Bonyadi, M.R., Wagner, M., Michalewicz, Z., Neumann, F.: A comprehensive benchmark set and heuristics for the traveling thief problem. In: GECCO 2014, pp. 477–484 (2014)
Polyakovskiy, S., Neumann, F.: Packing while traveling: mixed integer programming for a class of nonlinear knapsack problems. In: CPAIOR, pp. 332–346 (2015)
Polyakovskiy, S., Neumann, F.: The packing while traveling problem. Eur. J. Oper. Res. 258, 424–439 (2017)
Reinelt, G.: TSPLIB-a traveling salesman problem library. ORSA J. Comput. 4(3), 376–384 (1991)
Wagner, M.: Stealing items more efficiently with ants: a swarm intelligence approach to the travelling thief problem. In: Dorigo, M., Birattari, M., Li, X., López-Ibáñez, M., Ohkura, K., Pinciroli, C., Stützle, T. (eds.) ANTS 2016. LNCS, vol. 9882, pp. 273–281. Springer, Cham (2016). doi:10.1007/978-3-319-44427-7_25
Wagner, M., Lindauer, M., Misir, M., Nallaperuma, S., Hutter, F.: A case study of algorithm selection for the traveling thief problem. arXiv:1609.00462v1, pp. 1–23 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Moeini, M., Schermer, D., Wendt, O. (2017). A Hybrid Evolutionary Approach for Solving the Traveling Thief Problem. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10405. Springer, Cham. https://doi.org/10.1007/978-3-319-62395-5_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-62395-5_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-62394-8
Online ISBN: 978-3-319-62395-5
eBook Packages: Computer ScienceComputer Science (R0)