Hoang Nguyen
ABSTRACT
Many real-world optimization problems today are challenging to solve due to their inclusion of multiple interdependent NP-Hard subproblems. The Traveling Thief Problem (TTP), a relatively new combinatorial optimization problem, has been proposed to better model these types of problems. TTP comprises two well-known NP-Hard problems: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper introduces the SAVI algorithm, utilizing Simulated Annealing with a Vertex Insertion procedure that is efficiently implemented via Dynamic Programming. Experimental results show that SAVI runs efficiently across various TTP test cases, yielding highly competitive outcomes compared to other state-of-the-art algorithms, especially on medium and large-sized instances. Source code is available at https://github.com/ELO-Lab/SAVI.
- David L. Applegate, William J. Cook, and André Rohe. 2003. Chained Lin-Kernighan for Large Traveling Salesman Problems. INFORMS J. Comput. 15, 1 (2003), 82–92. https://doi.org/10.1287/ijoc.15.1.82.15157Google Scholar
Digital Library
- Mohammad Reza Bonyadi, Zbigniew Michalewicz, and Luigi Barone. 2013. The travelling thief problem: The first step in the transition from theoretical problems to realistic problems. In Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2013, Cancun, Mexico, June 20-23, 2013. IEEE, 1037–1044. https://doi.org/10.1109/CEC.2013.6557681Google ScholarCross Ref
- Mohammad Reza Bonyadi, Zbigniew Michalewicz, Michal Roman Przybylek, and Adam Wierzbicki. 2014. Socially inspired algorithms for the travelling thief problem. In Genetic and Evolutionary Computation Conference, GECCO ’14, Vancouver, BC, Canada, July 12-16, 2014. ACM, 421–428. https://doi.org/10.1145/2576768.2598367Google ScholarDigital Library
- Mohammad Reza Bonyadi, Zbigniew Michalewicz, Markus Wagner, and Frank Neumann. 2019. Evolutionary Computation for Multicomponent Problems: Opportunities and Future Directions. In Optimization in Industry, Present Practices and Future Scopes. Springer, 13–30. https://doi.org/10.1007/978-3-030-01641-8_2Google ScholarCross Ref
- Hayden Faulkner, Sergey Polyakovskiy, Tom Schultz, and Markus Wagner. 2015. Approximate Approaches to the Traveling Thief Problem. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, Madrid, Spain, July 11-15, 2015. ACM, 385–392. https://doi.org/10.1145/2739480.2754716Google ScholarDigital Library
- Arthur Guijt, Ngoc Hoang Luong, Peter A. N. Bosman, and Mathijs de Weerdt. 2022. On the impact of linkage learning, gene-pool optimal mixing, and non-redundant encoding on permutation optimization. Swarm Evol. Comput. 70 (2022), 101044. https://doi.org/10.1016/j.swevo.2022.101044Google ScholarCross Ref
- Darrall Henderson, Sheldon H. Jacobson, and Alan W. Johnson. 2003. The Theory and Practice of Simulated Annealing. In Handbook of Metaheuristics. International Series in Operations Research & Management Science, Vol. 57. Kluwer / Springer, 287–319. https://doi.org/10.1007/0-306-48056-5_10Google ScholarCross Ref
- Maksud Ibrahimov, Arvind Mohais, Sven Schellenberg, and Zbigniew Michalewicz. 2012. Evolutionary approaches for supply chain optimisation: Part I: single and two-component supply chains. Int. J. Intell. Comput. Cybern. 5, 4 (2012), 444–472. https://doi.org/10.1108/17563781211282231Google ScholarCross Ref
- Shen Lin and Brian W. Kernighan. 1973. An Effective Heuristic Algorithm for the Traveling-Salesman Problem. Oper. Res. 21, 2 (1973), 498–516. https://doi.org/10.1287/opre.21.2.498Google ScholarDigital Library
- Yi Mei, Xiaodong Li, and Xin Yao. 2014. Improving Efficiency of Heuristics for the Large Scale Traveling Thief Problem. In Simulated Evolution and Learning - 10th International Conference, SEAL 2014, Dunedin, New Zealand, December 15-18, 2014. Proceedings(Lecture Notes in Computer Science, Vol. 8886). Springer, 631–643. https://doi.org/10.1007/978-3-319-13563-2_53Google ScholarDigital Library
- Yi Mei, Xiaodong Li, and Xin Yao. 2014. On Investigation of Interdependence Between Sub-problems of the Travelling Thief Problem. Soft Computing 20 (10 2014). https://doi.org/10.1007/s00500-014-1487-2Google ScholarDigital Library
- Zbigniew Michalewicz and David B. Fogel. 2004. How to solve it - modern heuristics: second, revised and extended edition, Second Edition. Springer.Google ScholarCross Ref
- Sergey Polyakovskiy, Mohammad Reza Bonyadi, Markus Wagner, Zbigniew Michalewicz, and Frank Neumann. 2014. A comprehensive benchmark set and heuristics for the traveling thief problem. In Genetic and Evolutionary Computation Conference, GECCO ’14, Vancouver, BC, Canada, July 12-16, 2014. ACM, 477–484. https://doi.org/10.1145/2576768.2598249Google ScholarDigital Library
- Mohamed El Yafrani and Belaïd Ahiod. 2016. Population-based vs. Single-solution Heuristics for the Travelling Thief Problem. In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20 - 24, 2016. ACM, 317–324. https://doi.org/10.1145/2908812.2908847Google ScholarDigital Library
- Mohamed El Yafrani and Belaïd Ahiod. 2018. Efficiently solving the Traveling Thief Problem using hill climbing and simulated annealing. Inf. Sci. 432 (2018), 231–244. https://doi.org/10.1016/j.ins.2017.12.011Google ScholarCross Ref
- Zitong Zhang, Lei Yang, Peipei Kang, Xiaotian Jia, and Wensheng Zhang. 2021. Solving the Traveling Thief Problem Based on Item Selection Weight and Reverse-Order Allocation. IEEE Access 9 (2021), 54056–54066. https://doi.org/10.1109/ACCESS.2021.3070204Google ScholarCross Ref
- Donald W. Zimmerman and Bruno D. Zumbo. 1993. Relative Power of the Wilcoxon Test, the Friedman Test, and Repeated-Measures ANOVA on Ranks. The Journal of Experimental Education 62, 1 (1993), 75–86.Google ScholarCross Ref
Index Terms
- Simulated Annealing with Dynamic Programming-based Vertex Insertion for Efficiently Solving the Traveling Thief Problem
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