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Global and Local Moth-flame Optimization Algorithm for UAV Formation Path Planning Under Multi-constraints

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  • Intelligent Control and Applications
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Abstract

To improve the global and local search ability of moth-flame optimization algorithm, three optimization strategies are proposed in this paper, namely chaos-based moth initialization, adaptive weighted position update strategy and population diversity improvement strategy. In moth initialization process, chaos-based Logistic map is adopted to improve population diversity. Then, a nonlinear weighting factor is introduced into the spiral function to adaptively balance the global and local search ability. Besides, new moth is generated by population diversity improvement strategy, which improves diversity and optimality of the population. Finally, simulation tests of unmanned aerial vehicle (UAV) formation under multi-constraints are carried out and comparison results show that the proposed global and local moth-flame optimization algorithm has the superiority in rapidity and optimality in UAV path planning problem compared with the latest path planning algorithms.

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Authors and Affiliations

  1. School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei, 050018, China

    Xiao-Jing Wu, Lei Xu, Ran Zhen & Xue-Li Wu

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  1. Xiao-Jing Wu
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  2. Lei Xu
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  3. Ran Zhen
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Correspondence to Xue-Li Wu.

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This work is funded by National Natural Science Foundation of China (NO. 62003129), National Defense Basic Scientific Research Project of China (NO. JC***), Key research and development project of Hebei province (NO. 19250801D), and Graduate Research and Innovation Projects of Hebei Province (NO. CXZZSS2020098).

Xiao-Jing Wu received her B.S. and M.S. degrees from Yanshan University, in 2006 and 2009, respectively. She received her Ph.D. degree in control theory and control engineering from Yanshan University, China. Now, she is an Associate Professor at Hebei University of Science and Technology, China. Her research interests are in nonlinear control, UAV control, and path planning.

Lei Xu received his B.S. degree from Hebei University of Engineering in 2017. He is currently pursuing an M.S. degree in Electrical Engineering with the Department of Electrical Engineering, Hebei University of Science and Technology, China. His research interest is in path planning of UAV.

Ran Zhen received her B.S. degree from North China University of Science and Technology University in 1994. She received her M.S. degree from Huazhong University of Science and Technology in 2004. She is currently a Professor in the Electrical Engineering at Hebei University of Science and Technology, China. Her research interests are in intelligent control for nonlinear systems.

Xue-Li Wu received his B.S. and M.S. degrees from Yanshan University in 1983 and 1988, respectively. He received his Ph.D. degree from Huazhong University of Science and Technology in 2005. He is currently a Professor and Dean in the Electrical Engineering at Hebei University of Science and Technology, China. His research interests are in intelligent control and UAV risk avoidance.

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Wu, XJ., Xu, L., Zhen, R. et al. Global and Local Moth-flame Optimization Algorithm for UAV Formation Path Planning Under Multi-constraints. Int. J. Control Autom. Syst. 21, 1032–1047 (2023). https://doi.org/10.1007/s12555-020-0979-3

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  • DOI: https://doi.org/10.1007/s12555-020-0979-3

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