Using  Ant Colony Optimization Algorithm (ACO) to determine the optimal parameters

naseer26 hussein

doi:10.71229/99zs8a17

Authors

Naseer Alwan Hussein Department of Cinema and Television Arts, University of Diyala, Diyala, Iraq

DOI:

https://doi.org/10.71229/99zs8a17

Keywords:

Travelling Salesman Problem,, Ant Colony Optimization, Parameter Tuning,, Genetic Algorithms, , Convergence Analysis

Abstract

Our proposed system Ant Colony Optimization (ACO) is a meta- heuristic introduced by Dorigo et al. [9] which uses ideas from nature to find solutions to instances of the Travelling Salesman Problem (TSP) and other combinatorial optimisation problems. In this paper we analyse the parameter settings of the ACO algorithm. These determine the behavior of each ant and are critical for fast convergence to near optimal solutions of a given problem instance. We classify TSP instances using three measures of complexity and uniformity. We describe experimental work that attempts to correlate ‘types’ of TSP problems with parameter settings for fast convergence. We found these optimal parameter settings to be highly problem specific and dependent on the required accuracy of the solution. This inspired us to explore techniques for automatically learning the optimal parameters for a given TSP instance. We devised and implemented a hybrid ACO algorithm, similar to the one independently developed in [16], which used a genetic algorithm in the early stages to ‘breed’ a population of ants possessing near optimal behavioural parameter settings for a given problem. This hybrid algorithm converges rapidly for a wide range of problems when given a population of ants with diverse behavioural parameter settings.

References

1. Wei, Xianyong. "Task scheduling optimization strategy using improved ant colony optimization algorithm in cloud computing." Journal of ambient intelligence and humanized computing (2020): 1-12.‏

2. Liu, Yahui, and Buyang Cao. "A novel ant colony optimization algorithm with Levy flight." Ieee Access 8 (2020): 67205-67213.‏

3. Gupta, Arushi, and Smriti Srivastava. "Comparative analysis of ant colony and particle swarm optimization algorithms for distance optimization." Procedia Computer Science 173 (2020): 245-253.‏

4. Deng, Wu, et al. "An effective improved co-evolution ant colony optimisation algorithm with multi-strategies and its application." International Journal of Bio-Inspired Computation 16.3 (2020): 158-170.‏

5. Ajeil, Fatin Hassan, et al. "Grid-based mobile robot path planning using aging-based ant colony optimization algorithm in static and dynamic environments." Sensors 20.7 (2020): 1880.‏

6. Crespi, Carolina, Rocco A. Scollo, and Mario Pavone. "Effects of different dynamics in an ant colony optimization algorithm." 2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI). IEEE, 2020.‏

7. Chen, Aoran, Hao Tan, and Yiyue Zhu. "Ant colony optimization algorithm and its application." 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022). Vol. 12259. SPIE, 2022.‏

8. Li, Shugang, et al. "A new fast ant colony optimization algorithm: the saltatory evolution ant colony optimization algorithm." Mathematics 10.6 (2022): 925.‏

9. González, Patricia, et al. "An efficient ant colony optimization framework for HPC environments." Applied Soft Computing 114 (2022): 108058.‏

10. Liu, Yifan, Xiaoming You, and Sheng Liu. "Multi-ant colony optimization algorithm based on hybrid recommendation mechanism." Applied Intelligence 52.8 (2022): 8386-8411.‏

11. Rezvanian, Alireza, S. Mehdi Vahidipour, and Ali Sadollah. "An overview of ant colony optimization algorithms for dynamic optimization problems." Optimization Algorithms-Classics and Recent Advances (2023).‏

12. Ni, Yun, et al. "Characteristics and optimization strategies of A* algorithm and ant colony optimization in global path planning algorithm." International Journal of Pattern Recognition and Artificial Intelligence 37.03 (2023): 2351006.‏

13. Faisal, Mohammed, and Fahad Albogamy. "Ant colony optimization algorithm enhancement for better performance." 2023 IEEE world AI IoT congress (AIIoT). IEEE, 2023.‏

14. Yilmaz Eroglu, Duygu, and Umut Akcan. "An adapted ant colony optimization for feature selection." Applied Artificial Intelligence 38.1 (2024): 2335098.‏

15. Han, Hongzhang, Jun Tang, and Zhengjun Jing. "Wireless sensor network routing optimization based on improved ant colony algorithm in the Internet of Things." Heliyon 10.1 (2024).‏

16. Blum, Christian. "Ant colony optimization: A bibliometric review." Physics of life reviews 51 (2024): 87-95.‏

17. Al-Rawashdeh, Rana, et al. "Enhancing DoS Detection in WSNs Using Enhanced Ant Colony Optimization Algorithm." IEEE Access (2024).

18. Mukhlif, Yasir Adil, et al. "Ant colony and whale optimization algorithms aided by neural networks for optimum skin lesion diagnosis: A thorough review." Mathematics 12.7 (2024): 1049.

19. Rahman, Farhan Wahyu Nur, et al. "Application of ant colony optimization algorithm in determining PID parameters in AC motor control." Brilliance: Research of Artificial Intelligence 4.2 (2024): 538-549.‏

20. Abdelmoaty, Ahmed Mohamed, and Ibrahim Ihab Ibrahim. "Comparative analysis of four prominent ant colony optimization variants: Ant system, rank-based ant system, max-min ant system, and ant colony system." arXiv preprint arXiv:2405.15397 (2024).

21. Li, Dongdong, and Lei Wang. "Research on mobile robot path planning based on improved Q-evaluation ant colony optimization algorithm." Engineering Applications of Artificial Intelligence 160 (2025): 111890.‏

22. Wang, Hua. "Dynamic path optimization of cold chain logistics based on improved ant colony algorithm from a low-carbon perspective." Sustainable Operations and Comput