Abstract
The Travelling Thief Problem is a complex logistics planning problem composed of the Travelling Salesman Problem and the Knapsack Problem. The Bi-objective TTP needs to optimize the two goals of the time spent on the journey and the total value of the item picked up. There are two dimensions to be considered in the solution space, which is more difficult to deal with. The original solution method has some limitations. This paper proposes a MOEA/D-ACO algorithm based on finite pheromone weights, which constructs distance-related weights when pheromone weights are initialized and adjusts the weights to improve the effect and efficiency of the ant’s search path. Then a new weighted Tchebycheff aggregation method was designed. By adjusting the parameters to control the proportion of the two aggregation methods, the advantages of weighted sum method and Tchebycheff aggregation method are integrated to improve the quality of the algorithm. Then we introduced the method of dynamic adjustment of ant neighbor number and adaptive mutation operator to improve the convergence speed and the quality of the solution. The experiment result indicates that our algorithm has competitive performance comparing with the well-known optimization algorithms GA£¬ISA, ISA-Local, and NSGA-II on Bi-objective TTP benchmark datasets.
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
Similar content being viewed by others
References
Bonyadi, M.R., Michalewicz, Z., Barone, L.: The travelling thief problem: the first step in the transition from theoretical problems to realistic problems. In: Evolutionary Computation. IEEE Press, Piscataway, pp. 1037–1044 (2013). https://doi.org/10.1109/CEC.2013.6557681
Gutin, G., Punnen, A.: The traveling salesman problem and its variations. Paradigms Comb. Optim. Prob. New Approaches 4, 193–205 (2007). https://doi.org/10.1002/9781118600207.ch7
Kellerer, H., Pferschy, U., Pisinger, D.: Introduction to NP-Completeness of Knapsack Problems, pp. 483–493. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24777-7_16
Ke, L., Zhang, Q., Battiti, R.: MOEA/D-ACO: a multiobjective evolutionary algorithm using decomposition and antcolony. Cybern. IEEE Trans. 43, 1845–1859 (2013). https://doi.org/10.1109/TSMCB.2012.2231860
Blank, J., Deb, K., Mostaghim, S.: Solving the bi-objective traveling thief problem with multi-objective evolutionary algorithms. In: Trautmann, H., et al. (eds.) EMO 2017. LNCS, vol. 10173, pp. 46–60. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54157-0_4
Chagas, J.B.C., Blank, J., Wagner, M., Souza, M.J.F., Deb, K.: A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem. J. Heuristics 27(3), 267–301 (2020). https://doi.org/10.1007/s10732-020-09457-7
Cheng, J., Zhang, G., Li, Z., et al.: Multi-objective ant colony optimization based on decomposition for bi-objective traveling salesman problems. Soft. Comput. 16, 597–614 (2012). https://doi.org/10.1007/s00500-011-0759-3
Hui, L., Zhang, Q.: Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Trans. Evol. Comput. 13, 284–302 (2009). https://doi.org/10.1109/TEVC.2008.925798
Wu, J., Polyakovskiy, S., Wagner, M. et al.: Evolutionary computation plus dynamic programming for the bi-objective travelling thief problem. In: Proceedings of the Genetic and Evolutionary Computation Conference. ACM, New York, pp. 777–784. (2018). https://doi.org/10.1145/3205455.3205488
Mei, M.A., Hecheng, L.I.: Hybrid genetic algorithm for solving new optimization model of TTP. Comput. Eng. Appl. 54(6), 156–160 (2018)
El Yafrani, M., Martins, M., Wagner, M., Ahiod, B., Delgado, M., Lüders, R.: A hyperheuristic approach based on low-level heuristics for the travelling thief problem. Genet. Program Evolvable Mach. 19(1–2), 121–150 (2017). https://doi.org/10.1007/s10710-017-9308-x
Acknowledgment
This work was partially supported by the Natural Science Foundation of Guangdong Province of China (Grant No. 2020A1515010691), Science and Technology Project of Guangdong Province of China (Grant No. 2018A0124), National Natural Science Foundation of China (Grant Nos. 61573157 and 61703170), and Guangdong Provincial Key Laboratory of Food Quality and Safety (Grant No. 2020B1212060059).The authors also gratefully acknowledge the reviewers for their helpful comments and suggestions that helped to improve the presentation.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yang, L., Jia, X., Xu, R., Cao, J. (2021). An MOEA/D-ACO Algorithm with Finite Pheromone Weights for Bi-objective TTP. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1453. Springer, Singapore. https://doi.org/10.1007/978-981-16-7476-1_40
Download citation
DOI: https://doi.org/10.1007/978-981-16-7476-1_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7475-4
Online ISBN: 978-981-16-7476-1
eBook Packages: Computer ScienceComputer Science (R0)