Vu Huynh
ABSTRACT
Thief Orienteering Problem (ThOP) is a multi-component problem with two interdependent sub-problems Knapsack Problem and Orienteering Problem. ACO++, a state-of-the-art metaheuristic for ThOP, combines the MAX-MIN Ant System (MMAS) algorithm for route construction, a randomized heuristic for packing plan creation, and the 2-opt method for local search. The excellent reported performance of ACO++, however, is obtained using different sets of parameter values that have been extensively fine-tuned for each specific group of problem instances. In this paper, we present a novel self-adaptive variant of ACO++. Without requiring a cumbersome tuning process, our approach employs adaptive mechanisms to adjust the parameters to each particular problem instance during the algorithm runtime. We also use a lazy evaporation technique and a hierarchical clustering procedure to improve the efficiency of ants exploring the search space. Among the 432 benchmark instances, our proposed Self-Adaptive Ant System with Hierarchical Clustering (SAAS-HC) produces superior results compared to previous state-of-the-art approaches. The source code is available at https://github.com/ELO-Lab/SAAS-HC.
- 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 2013 IEEE Congress on Evolutionary Computation. 1037–1044. https://doi.org/10.1109/CEC.2013.6557681Google Scholar
Cross Ref
- Mohammad Reza Bonyadi, Zbigniew Michalewicz, Markus Wagner, and Frank Neumann. 2019. Evolutionary Computation for Multicomponent Problems: Opportunities and Future Directions. Springer International Publishing, Cham, 13–30. https://doi.org/10.1007/978-3-030-01641-8_2Google ScholarCross Ref
- Jonatas B.C. Chagas and Markus Wagner. 2020. Ants can orienteer a thief in their robbery. Operations Research Letters 48, 6 (2020), 708–714. https://doi.org/10.1016/j.orl.2020.08.011Google ScholarCross Ref
- Jonatas B. C. Chagas and Markus Wagner. 2021. Efficiently solving the thief orienteering problem with a max–min ant colony optimization approach. Optimization Letters 16, 8 (Nov. 2021), 2313–2331. https://doi.org/10.1007/s11590-021-01824-yGoogle ScholarCross Ref
- Y. Crama, A. W. J. Kolen, and E. J. Pesch. 1995. Local search in combinatorial optimization. Springer Berlin Heidelberg, Berlin, Heidelberg, 157–174. https://doi.org/10.1007/BFb0027029Google ScholarCross Ref
- Leonardo M. Faêda and André G. Santos. 2020. A Genetic Algorithm for the Thief Orienteering Problem. In 2020 IEEE Congress on Evolutionary Computation (CEC). 1–8. https://doi.org/10.1109/CEC48606.2020.9185848Google ScholarDigital Library
- Bruce L. Golden, Larry Levy, and Rakesh Vohra. 1987. The orienteering problem. Naval Research Logistics (NRL) 34, 3 (1987), 307–318. https://doi.org/10.1002/1520-6750(198706)34:3<307::AID-NAV3220340302>3.0.CO;2-DGoogle ScholarCross Ref
- Nikolaus Hansen. 2006. The CMA Evolution Strategy: A Comparing Review. Springer Berlin Heidelberg, Berlin, Heidelberg, 75–102. https://doi.org/10.1007/3-540-32494-1_4Google ScholarCross Ref
- Manuel Iori, Juan-José Salazar-González, and Daniele Vigo. 2007. An Exact Approach for the Vehicle Routing Problem with Two-Dimensional Loading Constraints. Transportation Science 41, 2 (2007), 253–264. https://doi.org/10.1287/trsc.1060.0165 arXiv:https://doi.org/10.1287/trsc.1060.0165Google ScholarDigital Library
- Fionn Murtagh and Pedro Contreras. 2012. Algorithms for hierarchical clustering: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2, 1 (2012), 86–97.Google ScholarCross Ref
- Fionn Murtagh and Pedro Contreras. 2017. Algorithms for hierarchical clustering: an overview, II. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 7, 6 (2017), e1219.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 Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation (Vancouver, BC, Canada) (GECCO ’14). Association for Computing Machinery, New York, NY, USA, 477–484. https://doi.org/10.1145/2576768.2598249Google ScholarDigital Library
- Sergey Polyakovskiy and Rym M’Hallah. 2011. An Intelligent Framework to Online Bin Packing in a Just-In-Time Environment. In Modern Approaches in Applied Intelligence. Springer Berlin Heidelberg, Berlin, Heidelberg, 226–236.Google Scholar
- André G. Santos and Jonatas B.C. Chagas. 2018. The Thief Orienteering Problem: Formulation and Heuristic Approaches. In 2018 IEEE Congress on Evolutionary Computation (CEC). 1–9. https://doi.org/10.1109/CEC.2018.8477853Google ScholarDigital Library
- Pranav Shetty and Suraj Singh. 2021. Hierarchical clustering: a survey. International Journal of Applied Research 7, 4 (2021), 178–181.Google ScholarCross Ref
- Petr Stodola, Pavel Otřísal, and Kamila Hasilová. 2022. Adaptive Ant Colony Optimization with node clustering applied to the Travelling Salesman Problem. Swarm and Evolutionary Computation 70 (2022), 101056. https://doi.org/10.1016/j.swevo.2022.101056Google ScholarCross Ref
- Thomas Stützle and Holger H. Hoos. 2000. MAX–MIN Ant System. Future Generation Computer Systems 16, 8 (2000), 889–914. https://doi.org/10.1016/S0167-739X(00)00043-1Google ScholarCross Ref
Index Terms
- Self-Adaptive Ant System with Hierarchical Clustering for the Thief Orienteering Problem
Recommendations
An Enhanced Ant Colony System for the Team Orienteering Problem with Time Windows
ISCCS '11: Proceedings of the 2011 International Symposium on Computer Science and SocietyThe Team Orienteering Problem with Time Windows (TOPTW) is a combinatorial optimization problem arising both in industrial scheduling and in transportation. Ant Colony System (ACS) is a well-known metaheuristic framework, and many efficient algorithms ...
A new hybrid method based on Particle Swarm Optimization, Ant Colony Optimization and 3-Opt algorithms for Traveling Salesman Problem
The Traveling Salesman Problem (TSP) is one of the standard test problems used in performance analysis of discrete optimization algorithms. The Ant Colony Optimization (ACO) algorithm appears among heuristic algorithms used for solving discrete ...
A modified ant system to achieve better balance between intensification and diversification for the traveling salesman problem
Adaptive solution construction and pheromone updating strategies.Better balance between intensification and diversification in the search process.Adjustment of construction parameter values throughout the entire process.Easy extension by adding local ...
Comments