REFERENCES

1. Cerotti D, Distefano S, Merlino G, Puliafito A. A crowd-cooperative approach for intelligent transportation systems. IEEE Trans Intell Transp Syst 2017;18:1529-39.

2. Zhao Y, Zheng Z, Liu Y. Survey on computational-intelligence-based UAV path planning. Knowl Based Syst 2018;158:54-64.

3. Duchoň F, Babinec A, Kajan M, et al. Path planning with modified a star algorithm for a mobile robot. Procedia Eng 2014;96:59-69.

4. Zhang T, Zhu Y, Song J. Real-time motion planning for mobile robots by means of artificial potential field method in unknown environment. Ind Rob 2010;37:384-400.

5. Wang H, Yu Y, Yuan Q. Application of Dijkstra algorithm in robot path-planning. In: 2011 Second International Conference on Mechanic Automation and Control Engineering; 2011 Jul 15-17; Hohhot. IEEE; 2011. pp. 1067-97.

6. Wu PPY, Campbell D, Merz T. Multi-objective four-dimensional vehicle motion planning in large dynamic environments. IEEE Trans Syst Man Cybern B 2011;41:621-34.

7. Shorakaei H, Vahdani M, Imani B, Gholami A. Optimal cooperative path planning of unmanned aerial vehicles by a parallel genetic algorithm. Robotica 2016;34:823-36.

8. Roberge V, Tarbouchi M, Labonte G. Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans Industr Inform 2013;9:132-41.

9. Abeywickrama HV, Jayawickrama BA, He Y, Dutkiewicz E. Potential field based inter-UAV collision avoidance using virtual target relocation. In: 2018 IEEE 87th Vehicular Technology Conference (VTC Spring); 2018 Jun 03-06; Porto, Portugal. IEEE; 2018. p. 1-5.

10. Vanegas G, Samaniego F, Girbes V, Armesto L, Garcia-Nieto S. Smooth 3D path planning for non-holonomic UAVs. In: 2018 7th International Conference on Systems and Control (ICSC); 2018 Oct 24-26; Valencia, Spain. IEEE; 2018. p. 1-6.

11. Jain G, Yadav G, Prakash D, Shukla A, Tiwari R. MVO-based path planning scheme with coordination of UAVs in 3-D environment. J Comput Sci 2019;37:101060.

12. Wang L, Cai R, Lin M, Zhong Y. Enhanced list-based simulated annealing algorithm for large-scale traveling salesman problem. IEEE Access 2019;7:144366-80.

13. Li G, Li J. An improved tabu search algorithm for the stochastic vehicle routing problem with soft time windows. IEEE Access 2020;8:158115-24.

14. Kala R, Shukla A, Tiwari R. Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning. Artif Intell Rev 2010;33:307-27.

15. Dorigo M, Maniezzo V, Colorni A. Positive feedback as a search strategy. Tech Rep 1991;91-016. Available from: https://api.semanticscholar.org/CorpusID:16027138. [Last accessed on 15 Dec 2023].

16. Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B 1996;26:29-41.

17. Sttzle T, Hoos H. Improving the ant system: a detailed report on the MAX-MIN ant system. Available from: https://api.semanticscholar.org/CorpusID:14922469. [Last accessed on 15 Dec 2023].

18. Dorigo M, Gambardella LM. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1997;1:53-66.

19. Li P, Wang H, Li X. Improved ant colony algorithm for global path planning. AIP Conf Proc 2017;1820:080013.

20. Huang M, Ding P, Huan JX. Global path planning for mobile robot based on improved ant colony algorithms. Appl Mech Mater 2013;418:15-9.

21. Luo Q, Wang H, Zheng Y, He J. Research on path planning of mobile robot based on improved ant colony algorithm. Neural Comput Appl 2020;32:1555-66.

22. Chen Y, Wu J, He C, Zhang S. Intelligent warehouse robot path planning based on improved ant colony algorithm. IEEE Access 2023;11:12360-7.

23. Yi N, Xu J, Yan L, Huang L. Task optimization and scheduling of distributed cyber-physical system based on improved ant colony algorithm. Future Gener Comput Syst 2020;109:134-48.

24. Ning J, Zhang Q, Zhang C, Zhang B. A best-path-updating information-guided ant colony optimization algorithm. Inf Sci 2018;433:142-62.

25. Wang Y, Chen J, Ning W, et al. A time-sensitive network scheduling algorithm based on improved ant colony optimization. Ale Eng J 2021;60:107-14.

26. Miao C, Chen G, Yan C, Wu Y. Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm. Comput Ind Eng 2021;156:107230.

27. Lyridis DV. An improved ant colony optimization algorithm for unmanned surface vehicle local path planning with multi-modality constraints. Ocean Eng 2021;241:109890.

28. Hou W, Xiong Z, Wang C, Chen H. Enhanced ant colony algorithm with communication mechanism for mobile robot path planning. Robot Auton Syst 2022;148:103949.

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