Abstract
Real-world problems are often composed of multiple interdependent components. In this case, benchmark problems that do not represent that interdependence are not a good choice to assess algorithm performance. In recent literature, a benchmark problem called Travelling Thief Problem (TTP) was proposed to better represent real-world multi-component problems. TTP is a combination of two well-known problems: 0-1 Knapsack Problem (KP) and the Travelling Salesman Problem (TSP). This paper presents a genetic algorithm-based optimization approach called Multi-Component Genetic Algorithm (MCGA) for solving TTP. It aims to solve the overall problem instead of each sub-component separately. Starting from a solution for the TSP component, obtained by the Chained Lin-Kernighan heuristic, the MCGA applies the evolutionary process (evaluation, selection, crossover, and mutation) iteratively using different basic operators for KP and TSP components. The MCGA was tested on some representative instances of TTP available in the literature. The comparisons show that MCGA obtains competitive solutions in 20 of the 24 TTP instances with 195 and 783 cities.
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Notes
- 1.
The source code can be found at https://github.com/DanielKneipp/GeneticAlgorithmTravelingThiefProblem.
References
Applegate, D., Cook, W., Rohe, A.: Chained Lin-Kernighan for large traveling salesman problems. INFORMS J. Comput. 15(1), 82–92 (2003)
Bonyadi, M.R., Michalewicz, Z., Przybyŏek, M.R., Wierzbicki, A.: Socially inspired algorithms for the travelling thief problem. In: Proceedings of the 2014 Conference on Genetic and Evolutionary Computation, pp. 421–428. ACM (2014)
Bonyadi, M., Michalewicz, Z., Barone, L.: The travelling thief problem: the first step in the transition from theoretical problems to realistic problems. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 1037–1044, June 2013
Davis, L.: Applying adaptive algorithms to epistatic domains. IJCAI 85, 162–164 (1985)
Faulkner, H., Polyakovskiy, S., Schultz, T., Wagner, M.: Approximate approaches to the traveling thief problem. In: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference, pp. 385–392. ACM (2015)
Lourenço, N., Pereira, F.B., Costa, E.: An evolutionary approach to the full optimization of the traveling thief problem. In: Chicano, F., Hu, B., García-Sánchez, P. (eds.) EvoCOP 2016. LNCS, vol. 9595, pp. 34–45. Springer, Heidelberg (2016). doi:10.1007/978-3-319-30698-8_3
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, Heidelberg (2014). doi:10.1007/978-3-319-13563-2_53
Polyakovskiy, S., Bonyadi, M.R., Wagner, M., Michalewicz, Z., Neumann, F.: A comprehensive benchmark set and heuristics for the traveling thief problem. In: Proceedings of the 2014 Conference on Genetic and Evolutionary Computation, GECCO 2014, pp. 477–484. ACM, New York (2014). http://doi.acm.org/10.1145/2576768.2598249
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, Heidelberg (2016). doi:10.1007/978-3-319-44427-7_25
Watson, J., Ross, C., Eisele, V., Denton, J., Bins, J., Guerra, C., Whitley, D., Howe, A.: The traveling salesrep problem, edge assembly crossover, and 2-opt. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 823–832. Springer, Heidelberg (1998). doi:10.1007/BFb0056924
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This work has been supported in part by the Brazilian agencies CAPES, CNPq, and FAPEMIG.
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Vieira, D.K.S., Soares, G.L., Vasconcelos, J.A., Mendes, M.H.S. (2017). A Genetic Algorithm for Multi-component Optimization Problems: The Case of the Travelling Thief Problem. In: Hu, B., López-Ibáñez, M. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2017. Lecture Notes in Computer Science(), vol 10197. Springer, Cham. https://doi.org/10.1007/978-3-319-55453-2_2
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