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A Novel Improved Bat Algorithm in UAV Path Planning

Na Lin1, Jiacheng Tang1, Xianwei Li2,3, Liang Zhao1,*
School of computer science, Shenyang Aerospace University, Shenyang, 110000, China.
School of Information Engineering, Suzhou University, Suzhou, 215000, China.
Global Information and Telecommunication Institute, Waseda University, Tokyo, 169-0051, Japan.
* Corresponding Author: Liang Zhao. Email: .

Computers, Materials & Continua 2019, 61(1), 323-344. https://doi.org/10.32604/cmc.2019.05674

Abstract

Path planning algorithm is the key point to UAV path planning scenario. Many traditional path planning methods still suffer from low convergence rate and insufficient robustness. In this paper, three main methods are contributed to solving these problems. First, the improved artificial potential field (APF) method is adopted to accelerate the convergence process of the bat’s position update. Second, the optimal success rate strategy is proposed to improve the adaptive inertia weight of bat algorithm. Third chaos strategy is proposed to avoid falling into a local optimum. Compared with standard APF and chaos strategy in UAV path planning scenarios, the improved algorithm CPFIBA (The improved artificial potential field method combined with chaotic bat algorithm, CPFIBA) significantly increases the success rate of finding suitable planning path and decrease the convergence time. Simulation results show that the proposed algorithm also has great robustness for processing with path planning problems. Meanwhile, it overcomes the shortcomings of the traditional meta-heuristic algorithms, as their convergence process is the potential to fall into a local optimum. From the simulation, we can see also obverse that the proposed CPFIBA provides better performance than BA and DEBA in problems of UAV path planning.

Keywords

UAV path planning, bat algorithm, the optimal success rate strategy, the APF method, chaos strategy

Cite This Article

N. Lin, J. Tang, X. Li and L. Zhao, "A novel improved bat algorithm in uav path planning," Computers, Materials & Continua, vol. 61, no.1, pp. 323–344, 2019.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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