Relevant bibliographies by topics / Travelling thief problem

Academic literature on the topic 'Travelling thief problem'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Travelling thief problem"

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Namazi, Majid, Conrad Sanderson, M. A. Newton, and Abdul Sattar. "Surrogate Assisted Optimisation for Travelling Thief Problems." Proceedings of the International Symposium on Combinatorial Search 11, no. 1 (September 1, 2021): 111–15. http://dx.doi.org/10.1609/socs.v11i1.18542.

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The travelling thief problem (TTP) is a multi-component optimisation problem involving two interdependent NP-hard components: the travelling salesman problem (TSP) and the knapsack problem (KP). Recent state-of-the-art TTP solvers modify the underlying TSP and KP solutions in an iterative and interleaved fashion. The TSP solution (cyclic tour) is typically changed in a deterministic way, while changes to the KP solution typically involve a random search, effectively resulting in a quasi-meandering exploration of the TTP solution space. Once a plateau is reached, the iterative search of the TTP solution space is restarted by using a new initial TSP tour. We propose to make the search more efficient though an adaptive surrogate model (based on a customised form of Support Vector Regression) that learns the characteristics of initial TSP tours that lead to good TTP solutions. The model is used to filter out non-promising initial TSP tours, in effect reducing the amount of time spent to find a good TTP solution. Experiments on a broad range of benchmark TTP instances indicate that the proposed approach filters out a considerable number of non-promising initial tours, at the cost of missing only a small number of the best TTP solutions.
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Ali, Hamid, Muhammad Zaid Rafique, Muhammad Shahzad Sarfraz, Muhammad Sheraz Arshad Malik, Mohammed A. Alqahtani, and Jehad Saad Alqurni. "A novel approach for solving travelling thief problem using enhanced simulated annealing." PeerJ Computer Science 7 (March 16, 2021): e377. http://dx.doi.org/10.7717/peerj-cs.377.

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Real-world optimization problems are getting more and more complex due to the involvement of inter dependencies. These complex problems need more advanced optimizing techniques. The Traveling Thief Problem (TTP) is an optimization problem that combines two well-known NP-Hard problems including the 0/1 knapsack problem and traveling salesman problem. TTP contains a person known as a thief who plans a tour to collect multiple items to fill his knapsack to gain maximum profit while incurring minimum cost in a standard time interval of 600 s. This paper proposed an efficient technique to solve the TTP problem by rearranging the steps of the knapsack. Initially, the picking strategy starts randomly and then a traversal plan is generated through the Lin-Kernighan heuristic. This traversal is then improved by eliminating the insignificant cities which contribute towards profit adversely by applying the modified simulated annealing technique. The proposed technique on different instances shows promising results as compared to other state-of-the-art algorithms. This technique has outperformed on a small and medium-size instance and competitive results have been obtained in the context of relatively larger instances.
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Mei, Yi, Xiaodong Li, and Xin Yao. "On investigation of interdependence between sub-problems of the Travelling Thief Problem." Soft Computing 20, no. 1 (October 17, 2014): 157–72. http://dx.doi.org/10.1007/s00500-014-1487-2.

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Nugraha, Lanang Alun. "Optimasi Travelling Thief Problem Menggunakan Algoritma Tree Physiology Optimization Berbasis Hiper Heuristik." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 8, no. 4 (December 13, 2021): 1810–20. http://dx.doi.org/10.35957/jatisi.v8i4.1167.

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Travelling thief problem (TTP) merupakan gabungan dari permasalahan travelling salesman problem dan knapsack problem. travelling thief problem sendiri merupakan permasalahan NP-Hard sehingga permasalahan sebagian besar diselesaikan menggunakan algoritma heuristic dan terus berkembang seiring berjalanya waktu. Algoritma yang digunakan pada penelitian ini adalah simple random untuk pemilihan low level heuristic (LLH) dan tree physiology optimization (TPO) untuk langkah move acceptance dengan menggunakan model Hyper-Heuristics. Pada penelitian yang telah dilakukan sebelumnya algoritma TPO mampu menghasilkan nilai yang cukup kompetitif dengan waktu komputasi yang baik, sedangkan pemodelan Hyper-Heuristics dapat menghasilkan nilai yang konsisten pada data yang beragam. Penelitian diawali dengan memodelkan algoritma TPO menjadi Hyper-Heuristics dan diuji coba dengan data dari TSPLib. Dari hasil uji coba yang dilakukan dapat dilihat bagaimana performa algoritma baru pada data yang diuji. Berdasarkan hasil yang didapat dari penelitian ini dapat disimpulkan bahwa algoritma LLH TPO dapat mengolah data TTP dengan ukuran di bawah 100 dengan cukup baik terbukti dengan hasil yang lebih baik dari metode genetic programming based hyper-heuristic (GPHS) yang telah ada sebelumnya, namun pada data di atas 100 performa LLH TPO menurun jika dibandingkan dengan metode GPHS.
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Namazi, Majid, M. A. Newton, Abdul Sattar, and Conrad Sanderson. "A Profit Guided Coordination Heuristic for Travelling Thief Problems." Proceedings of the International Symposium on Combinatorial Search 10, no. 1 (September 1, 2021): 140–44. http://dx.doi.org/10.1609/socs.v10i1.18513.

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The travelling thief problem (TTP) is a combination of two interdependent NP-hard components: travelling salesman problem (TSP) and knapsack problem (KP). Existing approaches for TTP typically solve the TSP and KP components in an interleaved fashion, where the solution to one component is held fixed while the other component is changed. This indicates poor coordination between solving the two components and may lead to poor quality TTP solutions. For solving the TSP component, the 2-OPT segment reversing heuristic is often used for modifying the tour. We propose an extended and modified form of the reversing heuristic in order to concurrently consider both the TSP and KP components. Items deemed as less profitable and picked in cities earlier in the reversed segment are replaced by items that tend to be equally or more profitable and not picked in the later cities. Comparative evaluations on a broad range of benchmark TTP instances indicate that the proposed approach outperforms existing state-of-the-art TTP solvers.
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Maity, Alenrex, and Swagatam Das. "Efficient hybrid local search heuristics for solving the travelling thief problem." Applied Soft Computing 93 (August 2020): 106284. http://dx.doi.org/10.1016/j.asoc.2020.106284.

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El Yafrani, Mohamed, Marcella Martins, Markus Wagner, Belaïd Ahiod, Myriam Delgado, and Ricardo Lüders. "A hyperheuristic approach based on low-level heuristics for the travelling thief problem." Genetic Programming and Evolvable Machines 19, no. 1-2 (July 15, 2017): 121–50. http://dx.doi.org/10.1007/s10710-017-9308-x.

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Astudillo Guerra, Jean Pierre, Karim Ahmed, Ryan Maher, Eddie Ubri, and Jeremy Blum. "Game Design for Better Security of Combination Locks." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12706–12. http://dx.doi.org/10.1609/aaai.v36i11.21547.

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Dial locks are commonly used to secure a person’s items. Commercially available dial locks often use four or five wheels of letters, allowing a user to select a word as a combination. In order to evaluate the security of these locks, we create a game, with an instance created by the lock designer, and played by a lock owner and a thief. In the game, the lock owner chooses a word as a combination, and the thief creates a brute force strategy to try all possible combinations that yield words until the combination is found. To accomplish the task, the thief will solve a version of the Probabilistic Travelling Salesman Problem (PTSP) by creating an a priori tour through all the words a lock can create. The goal for the game designer, then, is to create a lock configuration that maximizes the expected length of the best possible PTSP tour. This paper describes a Genetic Algorithm (GA) approach to design a near-optimal game, i.e. a lock configuration that makes it as difficult for the thief to crack. An analysis of the output of the GA shows that the locks that the system creates are significantly more secure than both commercial locks, in the context of this game.
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El Yafrani, Mohamed, and Belaïd Ahiod. "A local search based approach for solving the Travelling Thief Problem: The pros and cons." Applied Soft Computing 52 (March 2017): 795–804. http://dx.doi.org/10.1016/j.asoc.2016.09.047.

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Hayat, Tasawar, Saima Noreen, and Nasir Ali. "Effect of an Induced Magnetic Field on the Peristaltic Motion of Phan-Thien-Tanner (PTT) Fluid." Zeitschrift für Naturforschung A 65, no. 8-9 (September 1, 2010): 665–76. http://dx.doi.org/10.1515/zna-2010-8-907.

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This article looks at the influence of an induced magnetic field on peristaltic motion of an incompressible fluid in a planar channel with non-conductive walls. Peristaltic flow is generated by a sinusoidal wave travelling down its walls. The problem formulation in a wave frame of reference moving with velocity of wave is established. Mathematical relations for the stream function, pressure gradient, magnetic force function, and axial induced magnetic field are constructed. The pressure rise and frictional force are discussed by performing numerical integration. Effects of many sundry parameters entering into the governing problem are examined by plotting graphs
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Dissertations / Theses on the topic "Travelling thief problem"

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Wu, Junhua. "Exact and heuristic approaches for multi-component optimisation problems." Thesis, 2018. http://hdl.handle.net/2440/115700.

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Modern real world applications are commonly complex, consisting of multiple subsystems that may interact with or depend on each other. Our case-study about wave energy converters (WEC) for the renewable energy industry shows that in such a multi-component system, optimising each individual component cannot yield global optimality for the entire system, owing to the influence of their interactions or the dependence on one another. Moreover, modelling a multi-component problem is rarely easy due to the complexity of the issues, which leads to a desire for existent models on which to base, and against which to test, calculations. Recently, the travelling thief problem (TTP) has attracted significant attention in the Evolutionary Computation community. It is intended to offer a better model for multicomponent systems, where researchers can push forward their understanding of the optimisation of such systems, especially for understanding of the interconnections between the components. The TTP interconnects with two classic NP-hard problems, namely the travelling salesman problem and the 0-1 knapsack problem, via the transportation cost that non-linearly depends on the accumulated weight of items. This non-linear setting introduces additional complexity. We study this nonlinearity through a simplified version of the TTP - the packing while travelling (PWT) problem, which aims to maximise the total reward for a given travelling tour. Our theoretical and experimental investigations demonstrate that the difficulty of a given problem instance is significantly influenced by adjusting a single parameter, the renting rate, which prompted our method of creating relatively hard instances using simple evolutionary algorithms. Our further investigations into the PWT problem yield a dynamic programming (DP) approach that can solve the problem in pseudo polynomial time and a corresponding approximation scheme. The experimental investigations show that the new approaches outperform the state-of-the-art ones. We furthermore propose three exact algorithms for the TTP, based on the DP of the PWT problem. By employing the exact DP for the underlying PWT problem as a subroutine, we create a novel indicator-based hybrid evolutionary approach for a new bi-criteria formulation of the TTP. This hybrid design takes advantage of the DP approach, along with a number of novel indicators and selection mechanisms to achieve better solutions. The results of computational experiments show that the approach is capable to outperform the state-of-the-art results.
Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2018
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Book chapters on the topic "Travelling thief problem"

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Wu, Junhua, Markus Wagner, Sergey Polyakovskiy, and Frank Neumann. "Exact Approaches for the Travelling Thief Problem." In Lecture Notes in Computer Science, 110–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68759-9_10.

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Sachdeva, Ragav, Frank Neumann, and Markus Wagner. "The Dynamic Travelling Thief Problem: Benchmarks and Performance of Evolutionary Algorithms." In Communications in Computer and Information Science, 220–28. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63823-8_27.

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Vieira, Daniel K. S., Gustavo L. Soares, João A. Vasconcelos, and Marcus H. S. Mendes. "A Genetic Algorithm for Multi-component Optimization Problems: The Case of the Travelling Thief Problem." In Evolutionary Computation in Combinatorial Optimization, 18–29. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55453-2_2.

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Wagner, Markus. "Stealing Items More Efficiently with Ants: A Swarm Intelligence Approach to the Travelling Thief Problem." In Lecture Notes in Computer Science, 273–81. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44427-7_25.

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Conference papers on the topic "Travelling thief problem"

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Gupta, Bishwas C., and V. Prem Prakash. "Greedy heuristics for the Travelling Thief Problem." In 2015 39th National Systems Conference (NSC). IEEE, 2015. http://dx.doi.org/10.1109/natsys.2015.7489116.

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Bonyadi, Mohammad Reza, Zbigniew Michalewicz, Michal Roman Przybylek, and Adam Wierzbicki. "Socially inspired algorithms for the travelling thief problem." In GECCO '14: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2576768.2598367.

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Ali, Fathelrahman, and Mohamedelfatih Mohamedkhair. "Hyper-Heuristic Approaches for the Travelling Thief Problem." In 2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). IEEE, 2021. http://dx.doi.org/10.1109/iccceee49695.2021.9429559.

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Kumari, Rani, and Kamal Srivastava. "Variable Neighbourhood Search for Bi-Objective Travelling Thief Problem." In 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2020. http://dx.doi.org/10.1109/icrito48877.2020.9198016.

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Yafrani, Mohamed El, Marcella S. R. Martins, Mehdi El Krari, Markus Wagner, Myriam R. B. S. Delgado, Belaïd Ahiod, and Ricardo Lüders. "A fitness landscape analysis of the travelling thief problem." In GECCO '18: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205455.3205537.

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Nieto-Fuentes, Ricardo, Carlos Segura, and S. Ivvan Valdez. "A Guided Local Search Approach for the Travelling Thief Problem." In 2018 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2018. http://dx.doi.org/10.1109/cec.2018.8477821.

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Herring, Daniel, Michael Kirley, and Xin Yao. "Responsive Multi-population Models for the Dynamic Travelling Thief Problem." In 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2020. http://dx.doi.org/10.1109/ssci47803.2020.9308388.

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El Yafrani, Mohamed, and Belaïd Ahiod. "Population-based vs. Single-solution Heuristics for the Travelling Thief Problem." In GECCO '16: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2908812.2908847.

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El Yafrani, Mohamed, and Belaid Ahiod. "Cosolver2B: An efficient local search heuristic for the Travelling Thief Problem." In 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA). IEEE, 2015. http://dx.doi.org/10.1109/aiccsa.2015.7507099.

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Laszczyk, Maciej, and Paweł B. Myszkowski. "A Specialized Evolutionary Approach to the bi-objective Travelling Thief Problem." In 2019 Federated Conference on Computer Science and Information Systems. IEEE, 2019. http://dx.doi.org/10.15439/2019f191.

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