Research on multi-UAVs route planning method based on improved bat optimization algorithm

References

• Chakri, A., Khelif, R., Benouaret, M., & Yang, X. S. (2017). New directional bat algorithm for continuous optimization problems. Expert Systems with Applications. 69, 159–17. https://doi.org/10.1016/j.eswa.2016.10.050
   Web of Science ®Google Scholar
• de Moraes, R. S., & de Freitas, E. P. (2018). Distributed control for groups of unmanned aerial vehicles performing surveillance missions and providing relay communication network services. Journal of Intelligent & Robotic Systems, 92(3), 645–656. https://doi.org/10.1007/s10846-017-0726-z
   Web of Science ®Google Scholar
• Eun, Y., & Bang, H. (2006). Cooperative control of multiple unmanned aerial vehicles using the potential field theory. Journal of Aircraft, 43(6), 1805–1814. https://doi.org/10.2514/1.20345
   Web of Science ®Google Scholar
• Fister, I., Fong, S., Brest, J., & Fister, I. (2014). A novel hybrid self-adaptive bat algorithm. The Scientific World Journal, 2014, 709738. https://doi.org/10.1155/2014/709738
   Google Scholar
• Gandomi, A. H., & Yang, X. S. (2014). Chaotic bat algorithm. Journal of Computational Science, 5(2), 224–232. https://doi.org/10.1016/j.jocs.2013.10.002
   Web of Science ®Google Scholar
• Gangwar, S., & Pathak, V. K. (2020). Dry sliding wear characteristics evaluation and prediction of vacuum casted marble dust (MD) reinforced ZA-27 alloy composites using hybrid improved bat algorithm and ANN. Materials Today Communications, 25, 101615. https://doi.org/10.1016/j.mtcomm.2020.101615
   Web of Science ®Google Scholar
• Guo, J., Gao, Y., & Cui, G. (2015). The path planning for mobile robot based on bat algorithm. International Journal of Automation and Control, 9(1), 50–60. https://doi.org/10.1504/IJAAC.2015.068041
   Web of Science ®Google Scholar
• Li, K., Han, Y., Ge, F., Ge, F., Xu, W., & Liu, L. (2020). Tracking a dynamic invading target by UAV in oilfield inspection via an improved bat algorithm. Applied Soft Computing, 90, 106150. https://doi.org/10.1016/j.asoc.2020.106150
   Web of Science ®Google Scholar
• Li, Z. J., & Liu, X. W. (2015). Cooperative path planning of multi-UAV based on evolutionary algorithm. Fire Control & Command Control, 40(2), 85–89. https://doi.org/10.3969/j.issn.1002-0640.2015.02.022
   Google Scholar
• Li, Z., Ma, L., & Zhang, H. (2014). Bat algorithm for the multi-objective 0-1 programming problem. Journal of Intelligent Systems, 9(6), 672–676. https://doi.org/10.3969/j.issn.1673-4785.201310038
   Google Scholar
• Lin, N., Tang, J., Li, X., & Zhao, L. (2019). A novel improved bat algorithm in UAV path planning. Computers, Materials & Continua, 61, 323–344. https://doi.org/10.32604/cmc.2019.05674
   Web of Science ®Google Scholar
• Liu, C. (2019). Method of path planning for multi-UAV based on improved genetic algorithm. Fire Control & Command Control, 44(1), 18–22. https://doi.org/10.3969/j.issn.1002-0640.2019.01.004
   Google Scholar
• Liu, R., Yang, F., & Zhang, H. (2018). Path planning for UAV based on improved chaotic ant colony algorithm (CACA). Command Information System and Technology, 9(6), 41–48. https://doi.org/10.15908/j.cnki.cist.2018.06.008
   Google Scholar
• Liu, K., Zhou, J. Q., & Guo, X. H. (2013). Path planning research for unmanned air vehicle based on improved particle swarm algorithm. Journal of North University of China (Natural Science Edition), 34(4), 441–447. https://doi.org/10.3969/j.issn.1673-3193.2013.04.019
   Google Scholar
• Li, G. C., & Xiao, Q. X. (2014). Cross-entropy-inspired bat algorithm for absolute value equation. Application Research of Computers, 31(10), 2965–296+2985. https://doi.org/10.3969/j.issn.1001-3695.2014.10.019
   Google Scholar
• Pathak, V. K., & Srivastava, A. K. (2020). A novel upgraded bat algorithm based on cuckoo search and Sugeno inertia weight for large scale and constrained engineering design optimization problems. Engineering with Computers, 1–28. https://doi.org/10.1007/s00366-020-01127-3
   Web of Science ®Google Scholar
• Redding, J., Amin, J., Boskovic, J., Kang, Y., Hedrick, K., Howlett, J., & Poll, S. (2007). A real-time obstacle detection and reactive path planning system for autonomous small-scale helicopters[C]//AIAA Guidance, Navigation and Control Conference and Exhibit. 2007:6413. https://doi.org/10.2514/6.2007-6413.
   Google Scholar
• Shi, Y., & Eberhart, R. C. (1999). Empirical study of particle swarm optimization[C]//Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406). IEEE, 3, 1945–1950. https://doi.org/10.1109/CEC.1999.785511
   Google Scholar
• Sun, S., & Sun, T. (2022). Research on UAV path planning based on fusion A* algorithm. Electronic Measurement Technology, 45(9), 82–91. https://doi.org/10.19651/j.cnki.emt.2208770
   Google Scholar
• Wang, M. Q. (2011). Collaborative path planning of multiple UAV based on ant colony algorithm. Computer Engineering, 37(S1), 176–178.
   Google Scholar
• Wang, L. J., Yin, Y. L., & Zhong, Y. W. (2013). Cuckoo search algorithm with dimension by dimension improvement. Journal of Software, 24(11), 2687–2698. https://doi.org/10.3724/SP.J.1001.2013.04476
   Google Scholar
• Xi, Z., Wang, J., & Yang, Q. (2018). Optimal path planning for UAVs based on an improved bat algorithm[C]//Proceedings of international multi-conference on complexity, Informatics and cybernetics (pp. 40–45).
   Google Scholar
• Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm[M]//Nature inspired cooperative strategies for optimization (NICSO 2010). Heidelberg.
   Google Scholar
• Yuan, X., Yuan, X., & Liu, Z. (2021). Hybrid path planning based on bat algorithm and dynamic window method. Experimental Technology and Management, 38(10), 177–192. https://doi.org/10.16791/j.cnki.sjg.2021.10.033
   Google Scholar
• Zhou, X., Gao, F., Fang, X., & Lan, Z. (2021). Improved bat algorithm for UAV path planning in three-dimensional space. IEEE Access, 9, 20100–20116. https://doi.org/10.1109/ACCESS.2021.3054179
   Web of Science ®Google Scholar