Abstract
Vehicle routing problem is a combinatorial optimization problem. Ant system, one of the most popular algorithms for solving the combinatorial optimization problem, is inspired by the ant foraging behavior. However, Ant system has the problem of premature convergence and easily getting trapped into local optimum. This paper proposes the modified ant system with threshold. In the beginning, the proposed algorithm randomly generates the initial population. To obtain a more diverse population, the transition probability and the threshold are used to determine which city should be next in the path. Moreover, the improved pheromone updating process is introduced to help reducing the rate of convergence. Three local searches, swap, insert, and reverse, are used in the proposed algorithm to prevent getting trapped in a local optimum. The datasets used in this research were taken from TSPLIB, the standard benchmark datasets for TSP. The performance of the algorithm was compared with that of the other 3 algorithms: GA-PSO-ACO, FOGS-ACO, and Hybrid VNS. The results on 20 datasets showed that the proposed method outperformed or at least at par with the others on 15 datasets. On the other hand, there was one dataset, rd100, on which the proposed algorithm was better than the best-known solution. According to the result, our modified ant system with threshold was very effective for the small- and medium-sized vehicle routing 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
Mahi, M., et al.: A new hybrid method based on particle swarm optimization, ant colony optimization and 3-opt algorithms for traveling salesman problem. Appl. Soft Comput. 30, 484–490 (2015)
Ezugwu, A.E.-S., Adewumi, A.O.: Discrete symbiotic organisms search algorithm for travelling salesman problem. Expert Syst. App. 87, 70–78 (2017)
Sedighizadeh, D., Mazaheripour, H.: Optimization of multi objective vehicle routing problem using a new hybrid algorithm base on particle swarm optimization and artificial bee colony algorithm considering precedence constraints. Alexamdria Eng. J. 57(4), 2225–2239 (2017)
Osaba, E., et al.: A discrete firefly algorithm to solve a rich vehicle routing problem modeling a newspaper distribution system with recycling policy. Soft. Comput. 21, 5295–5308 (2017)
Goel, R., Maini, R.: A hybrid of ant colony and firefly algorithm (HAFA) for solving vehicle routing problems. J. Comput. Sci. 25, 28–37 (2018)
Pop, P.C., et al.: A novel two-level optimization approach for clustered vehicle routing problem. Comput. Ind. Eng. 115, 304–318 (2018)
Liao, E., Liu, C.: A hierarchical algorithm based on density peaks clustering and ant colony optimization for traveling salesman problem. IEEE Access 6, 38921–38933 (2018)
Chen, M.H., et al.: A hybrid two-stage sweep algorithm for capacitated vehicle routing problem. In: International Conference on Control, Automation and Robotics 2015, Automation and Robotics (ICCAR), IEEE (2015)
Zao, G., et al.: A modified max-min ant system for vehicle routing problems. In: Proceedings of the 4th International Conference on Wireless Communications, Networking and Mobile Computing (2008)
TSPLIB. http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp/. Accessed 30 Dec 2021
Deng, W., et al.: A novel two-stage hybrid swarm intelligence optimization. Soft. Comput. 16, 1707–1722 (2012)
Saenphon, T., et al.: Combining new fast opposite gradient search with ant colony optimization for solving travelling salesman problem. Eng. Appl. Artif. Intell. 35, 324–334 (2014)
Hore, S., et al.: Improving variable neighborhood search to solve the travelling salesman problem. Appl. Soft Comput. 68, 83–91 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rungwachira, P., Thammano, A. (2022). Modified Ant System with Threshold for the Vehicle Routing Problem. In: Meesad, P., Sodsee, S., Jitsakul, W., Tangwannawit, S. (eds) Proceedings of the 18th International Conference on Computing and Information Technology (IC2IT 2022). IC2IT 2022. Lecture Notes in Networks and Systems, vol 453. Springer, Cham. https://doi.org/10.1007/978-3-030-99948-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-99948-3_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-99947-6
Online ISBN: 978-3-030-99948-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)