Solving the Travelling Salesman Problem by Using Artificial Bee Colony Algorithm

Authors

  • Siti Hafawati Jamaluddin Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis Branch
  • Noor Ainul Hayati Mohd Naziri Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis Branch
  • Norwaziah Mahmud Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis Branch
  • Nur Syuhada Muhammat Pazil Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Melaka Branch

DOI:

https://doi.org/10.24191/jcrinn.v7i2.295

Keywords:

Travelling Salesman Problem, Artifical Bee Colony Algorithm, Optimisation

Abstract

Travelling Salesman Problem (TSP) is a list of cities that must visit all cities that start and end in the same city to find the minimum cost of time or distance. The Artificial Bee Colony (ABC) algorithm was used in this study to resolve the TSP. ABC algorithms is an optimisation technique that simulates the foraging behaviour of honey bees and has been successfully applied to various practical issues. ABC algorithm has three types of bees that are used by bees, onlooker bees, and scout bees. In Bavaria from the Library of Traveling Salesman Problem, the distance from one city to another has been used to find the best solution for the shortest distance. The result shows that the best solution for the shortest distance that travellers have to travel in all the 29 cities in Bavaria is 3974km.

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References

Akhand, M. A. H., Ayon, S. I., Shahriyar, S. A., Siddique, N., & Adeli, H. (2019). Discrete Spider Monkey Optimization for Traveling Salesman Problem. Applied Soft Computing, 105887.

Guo, Y., Li, X., Tang, Y., & Li, J. (2017). Heuristic artificial bee colony algorithm for uncovering community in complex networks. Mathematical Problems in Engineering, 2017.

Kaspi, M., Zofi, M., & Teller, R. (2019). Maximising the Profit per Unit Time for the Travelling Salesman Problem. Computers & Industrial Engineering.

Khamis, N., Selamat, H., Ismail, F. S., Lutfy, O. F., Haniff, M. F., & Nordin, I. N. A. M. (2019). Optimised exit door locations for a safer emergency evacuation using crowd evacuation model and artificial bee colony optimisation. Chaos, Solitons & Fractals, 109505.

Khan, I., & Maiti, M. K. (2019). A swap sequence based artificial bee colony algorithm for traveling salesman problem. Swarm and evolutionary computation, 44, 428-438.

Lvshan, Y., Dongzhi, Y., & Weiyu, Y. (2017, November). Artificial bee colony algorithm with genetic algorithm for job shop scheduling problem. In 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) (pp. 433-438). IEEE.

Mridula, K. M., Rahman, N., & Ameer, P. M. (2018). Sound velocity profile estimation using ray tracing and nature inspired meta-heuristic algorithms in underwater sensor networks. IET Communications, 13(5), 528-538.

O’Neil, R. J., & Hoffman, K. (2019). Decision diagrams for solving traveling salesman problems with pickup and delivery in real time. Operations Research Letters, 47(3), 197-201.

Pandiri, V., & Singh, A. (2018). A hyper-heuristic based artificial bee colony algorithm for k-Interconnected multi-depot multi-traveling salesman problem. Information Sciences, 463, 261-281.

Xu, J., Pei, L., & Zhu, R. Z. (2018). Application of a genetic algorithm with random crossover and dynamic mutation on the travelling salesman problem. Procedia computer science, 131, 937-945.

Zuloaga, M. S., & Moser, B. R. (2017, July). Optimising resource allocation in a portfolio of projects related to technology infusion using heuristic and meta-heuristic methods. In 2017 Portland International Conference on Management of Engineering and Technology (PICMET) (pp. 1-23). IEEE.

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Published

2022-09-30

How to Cite

Jamaluddin, S. H., Mohd Naziri, N. A. H., Mahmud, N., & Muhammat Pazil, N. S. (2022). Solving the Travelling Salesman Problem by Using Artificial Bee Colony Algorithm. Journal of Computing Research and Innovation, 7(2), 121–131. https://doi.org/10.24191/jcrinn.v7i2.295

Issue

Section

General Computing

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