CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Applications of Ant Colony Optimization in Control Systems, Robotics and Vision

by Himanshu Jahagirdar, Vilas Dhuri, Pratik Bobade, Rajani Mangala
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 23
Year of Publication: 2019
Authors: Himanshu Jahagirdar, Vilas Dhuri, Pratik Bobade, Rajani Mangala
10.5120/ijca2019919679

Himanshu Jahagirdar, Vilas Dhuri, Pratik Bobade, Rajani Mangala . Applications of Ant Colony Optimization in Control Systems, Robotics and Vision. International Journal of Computer Applications. 177, 23 ( Dec 2019), 15-19. DOI=10.5120/ijca2019919679

@article{ 10.5120/ijca2019919679,
author = { Himanshu Jahagirdar, Vilas Dhuri, Pratik Bobade, Rajani Mangala },
title = { Applications of Ant Colony Optimization in Control Systems, Robotics and Vision },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2019 },
volume = { 177 },
number = { 23 },
month = { Dec },
year = { 2019 },
issn = { 0975-8887 },
pages = { 15-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number23/31036-2019919679/ },
doi = { 10.5120/ijca2019919679 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:46:41.357246+05:30
%A Himanshu Jahagirdar
%A Vilas Dhuri
%A Pratik Bobade
%A Rajani Mangala
%T Applications of Ant Colony Optimization in Control Systems, Robotics and Vision
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 23
%P 15-19
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Swarm Intelligence is driving research in multi-agent system based robotic and mobile control applications. A swarm optimization algorithm- Ant Colony Optimization (ACO) provides a stochastic ‘shortest path’ approach inspired by ant colonies to obtain global solution in an optimization problem. This paper reviews the impact of ACO in robotics, computer vision, control systems and sensor networks for autonomous systems. The performance of ACO as a metaheuristic or general optimization is discussed with respect to its consequences on parameters like energy efficiency, time for convergence, route selection, etc.

References
  1. M. Dorigo, V. Maniezzo, A. Colorni, "Ant System: Optimization by a colony of cooperating agents", IEEE Transactions on Systems Man and Cybernetics-Part B, vol. 26, no. 1, pp. 29-41, 1996.
  2. Thomas STUTZLE ̈ ‡ and Marco DORIGO IRIDIA, Université Libre de Bruxelles, Belgium {t stutzle,m dorigo}
  3. Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem Marco Dorigo and Luca Maria Gambardella
  4. Baterina, Anna Veronica and Carlos Oppus. “Image edge detection using ant colony optimization.” (2010).
  5. Jing Tian, Weiyu Yu, & Shengli Xie. (2008). An ant colony optimization algorithm for image edge detection. 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). doi:10.1109/cec.2008.4630880
  6. Garcia, M. A. P., Montiel, O., Castillo, O., Sepúlveda, R., & Melin, P. (2009). Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation. Applied Soft Computing, 9(3), 1102–1110. doi:10.1016/j.asoc.2009.02.014
  7. H. Nezamabadi-Pour, S. Saryazdi, and E. Rashedi, “Edge detection using ant algorithms,” Soft Computing, vol. 10, pp. 623–628, May 2006.
  8. Purian, F. K., & Sadeghian, E. (2013). Mobile robots path planning using ant colony optimization and Fuzzy Logic algorithms in unknown dynamic environments. 2013 International Conference on Control, Automation, Robotics and Embedded Systems (CARE). doi:10.1109/care.2013.6733718
  9. Rashid, R., Perumal, N., Elamvazuthi, I., Tageldeen, M. K., Khan, M. K. A. A., & Parasuraman, S. (2016). Mobile robot path planning using Ant Colony Optimization. 2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA). doi:10.1109/roma.2016.7847836
  10. Liu, J., Yang, J., Liu, H., Tian, X., & Gao, M. (2016). An improved ant colony algorithm for robot path planning. Soft Computing, 21(19), 5829–5839. doi:10.1007/s00500-016-2161-7
  11. Guoliang Chen and Jie Liu, “Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential FieldMethod,” Computational Intelligence and Neuroscience, vol.2019,ArticleID1932812,10pages,2019.
  12. https://doi.org/10.1155/2019/1932812.
  13. M. Dorigo. (2007) Ant Colony Optimization, Scholarpedia,2(3):1461.[Online].Available:
  14. http://www.scholarpedia.org/article/Ant_colony_optimization.
  15. M. Dorigo, V. Maniezzo, and A. Colorni, Ant System: Optimization by a Colony of Cooperating Agents, IEEE Transactions on Systems, Man and Cybernetics - Part B, vol. 26, pp. 29-41, 1996.
  16. Xu, Q.-L., Cai, M.-M., & Zhao, L.-H. (2017). The robot path planning based on ant colony and particle swarm fusion algorithm. 2017 Chinese Automation Congress (CAC). doi:10.1109/cac.2017.8242802
  17. Lu, D.-S., & Chen, C.-C. (2008). Edge detection improvement by ant colony optimization. Pattern Recognition Letters, 29(4), 416–425. doi:10.1016/j.patrec.2007.10.021
  18. Ibtissem Chiha, Noureddine Liouane, and Pierre Borne, “Tuning PID Controller Using Multiobjective Ant Colony Optimization,” Applied Computational Intelligence and Soft Computing, vol. 2012, Article ID 536326, 7 pages, 2012
  19. J. G. Ziegler and N. B. Nichols, “Optimum settings for automatic controllers,” Transactions of the ASME, vol. 64, pp. 759– 768, 1942
  20. A. Bagis, “Determination of the PID controller parameters by modified genetic algorithm for improved performance,” Journal of Information Science and Engineering, vol. 23, no. 5, pp. 1469– 1480, 2007.
  21. Van Ast, J., Babuska, R., & De Schutter, B. (2009). Fuzzy Ant
  22. Colony Optimization for optimal control. 2009 doi:10.1109/acc.2009.5160327
  23. J. M. van Ast, R. Babuska, and B. De Schutter, “Ant colony opti- ˇ mization for optimal control,” in Proceedings of the 2008 Congress on Evolutionary Computation (CEC 2008), Hong Kong, China, June 2008, pp. 2040–2046.
  24. Liao, W.-H., Kao, Y., & Fan, C.-M. (2008). Data aggregation in wireless sensor networks using ant colony algorithm. Journal of Network and Computer Applications, 31(4), 387–401. doi:10.1016/j.jnca.2008.02.006
  25. Misra R, Mandal C. Ant-aggregation: ant colony algorithm for optimal data aggregation in wireless sensor networks. In: International Conference on Wireless and Optical Communications Networks, 2006
  26. Bell, J. E., & McMullen, P. R. (2004). Ant colony optimization techniques for the vehicle routing problem.Advanced Engineering Informatics, 18(1), 41–48. doi:10.1016/j.aei.2004.07.001
  27. Yu, Bin, Zhongzhen Yang and Baozhen Yao. “An improved ant colony optimization for vehicle routing problem.” European Journal of Operational Research 196 (2009): 171-176.
  28. Yu, B., & Yang, Z. Z. (2011). An ant colony optimization model: The period vehicle routing problem with time windows. Transportation Research Part E: Logistics and Transportation Review, 47(2), 166–181. doi:10.1016/j.tre.2010.09.010
  29. Haitao Xu, Pan Pu, and Feng Duan, “Dynamic Vehicle Routing Problems with Enhanced Ant Colony Optimization,” Discrete Dynamics in Nature and Society, vol. 2018, Article ID 1295485, 13 pages, 2018. https://doi.org/10.1155/2018/1295485.
  30. Kammoun, H. M., Kallel, I., Alimi, A. M., & Casillas, J. (2010). An adaptive vehicle guidance system instigated from ant colony behavior. 2010 IEEE International ConferenceonSystems,ManandCybernetics.doi:10.1109/icsmc.2010.5642001
  31. Wu, J., Abbas-Turki, A., & El Moudni, A. (2011). Cooperative driving: an ant colony system for autonomous intersection management. Applied Intelligence, 37(2), 207–222. doi:10.1007/s10489-011-0322-z
  32. Camilo, T., Carreto, C., Silva, J. S., & Boavida, F. (2006). An Energy-Efficient Ant-Based Routing Algorithm for Wireless Sensor Networks. Lecture Notes in Computer Science, 49–59. doi:10.1007/11839088_5
  33. Li, Jishun & Zheng, Guoqiang & Liang, Zhao. (2013). Optimization of an Intelligent Controller for Parallel Autonomous Parking. TELKOMNIKA : Indonesian Journal of Electrical Engineering. 11. 10.11591/telkomnika.v11i2.1404.
  34. Zheng, Guoqiang, Zhao Liang and Jishun Li. “Optimization of an Intelligent Controller for Parallel Autonomous Parking.” (2012).
  35. Arnay, R., Acosta, L., Sigut, M., & Toledo, J. (n.d.). Applying an Ant Colony Optimization Algorithm to an Artificial Vision Problem in a Robotic Vehicle. International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008), 490–497. doi:10.1007/978-3-540-85863-8_57
  36. Bolun Chen, Ling Chen, Yixin Chen, Efficient ant colony optimization for image feature selection, Signal Processing, Volume 93, Issue 6,2013, Pages 1566-1576, ISSN 0165-1684,https://doi.org/10.1016/j.sigpro.2012.10.022.
  37. Shortest Path Finding Algorithm Using Ant Colony Optimization Er. Sarbjeet Kaur. International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 www.ijert.org IJERT Vol. 2 Issue 6, June - 2013
  38. Zhou Hao, Xuejie Zhang, Pengfei Yu, & Haiyan Li. (2010). Video object tracing based on particle filter with ant colony optimization. 2010 2nd International Conference on Advanced Computer Control doi:10.1109/icacc.2010.5486857
  39. Aghababa, M. P. (2012). 3D path planning for underwater vehicles using five evolutionary optimization algorithms avoiding static and energetic obstacles. Applied Ocean Research, 38, 48–doi:10.1016/j.apor.2012.06.002
  40. Ma, Y.-N., Gong, Y., Xiao, C.-F., Gao, Y., & Zhang, J. (2018). Path Planning for Autonomous Underwater Vehicles: An Ant Colony Algorithm Incorporating Alarm Pheromone. IEEETransactions on Vehicular Technology, 1–1. doi:10.1109/tvt.2018.2882130
  41. Chao Zhang, Zhen, Z., Daobo Wang, & Meng Li. (2010). UAV path planning method based on ant colony optimization. 2010 Chinese Control and Decision Conference. doi:10.1109/ccdc.2010.5498477
  42. Cekmez, U., Ozsiginan, M., & Sahingoz, O. K. (2014). A UAV path planning with parallel ACO algorithm on CUDA platform. 2014 International Conference on Unmanned Aircraft Systems (ICUAS). doi:10.1109/icuas.2014.6842273
  43. Duan, H., Zhang, X., Wu, J., & Ma, G. (2009). Max-Min Adaptive Ant Colony Optimization Approach to Multi-UAVs Coordinated Trajectory Replanning in Dynamic and Uncertain Environments. Journal of Bionic Engineering, 6(2), 161–173. doi:10.1016/s1672-6529(08)60113-4
  44. Perez-Carabaza, S., Besada-Portas, E., Lopez-Orozco, J. A., de la Cruz, J. M. (2018). Ant colony optimization for multi-UAV minimum time search in uncertain domains. Applied SoftComputing,62,789–806. doi:10.1016/j.asoc.2017.09.00
Index Terms

Computer Science
Information Sciences

Keywords

Ant Colony Optimization Robotics Mobile Robots

Powered by PhDFocusTM