Wang Shuxin, Xu Bingruo, Zheng Yejun, Yue Yinggao, Xiong Mengji
School of Intelligent Manufacturing, Shanghai Zhongqiao Vocational and Technical University, Shanghai 201514, China.
School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China.
Biomimetics (Basel). 2025 May 11;10(5):310. doi: 10.3390/biomimetics10050310.
The Black-winged Kite Optimization Algorithm (BKA) is likely to experience a sluggish convergence rate when confronted with the optimization of complex multimodal functions. The fundamental algorithm has a tendency to get stuck in local optima, thus rendering it arduous to identify the global optimal solution. When dealing with large-scale data or high-dimensional optimization challenges, the BKA algorithm entails significant computational expenses, which might lead to excessive memory usage or prolonged running durations. In order to enhance the BKA and tackle these problems, a revised Black-winged Kite Optimization Algorithm (TGBKA) that incorporates the Tent chaos mapping and Gaussian mutation strategies is put forward. The algorithm is simulated and analyzed alongside other swarm intelligence algorithms by utilizing the CEC2017 test function set. The optimization outcomes of the test functions and the function convergence curves indicate that the TGBKA demonstrates superior optimization precision, a quicker convergence speed, as well as robust anti-interference and environmental adaptability. It is also contrasted with numerous similar algorithms via simulation experiments in various scene models for Unmanned Aerial Vehicle (UAV) path planning. In comparison to other algorithms, the TGBKA produces a shorter flight route, a higher convergence speed, and stronger adaptability to complex environments. It is capable of efficiently addressing UAV path planning issues and improving the UAV's path planning abilities.
黑翅鸢优化算法（BKA）在面对复杂多峰函数的优化时，可能会出现收敛速度缓慢的情况。该基本算法容易陷入局部最优，因此难以找到全局最优解。在处理大规模数据或高维优化挑战时，BKA算法需要大量的计算开销，这可能导致内存使用过多或运行时间过长。为了改进BKA并解决这些问题，提出了一种结合帐篷混沌映射和高斯变异策略的改进黑翅鸢优化算法（TGBKA）。利用CEC2017测试函数集，将该算法与其他群体智能算法一起进行了仿真和分析。测试函数的优化结果和函数收敛曲线表明，TGBKA具有更高的优化精度、更快的收敛速度以及强大的抗干扰能力和环境适应性。通过在各种无人机（UAV）路径规划场景模型中的仿真实验，还将其与众多类似算法进行了对比。与其他算法相比，TGBKA生成的飞行路线更短、收敛速度更快，并且对复杂环境的适应性更强。它能够有效地解决无人机路径规划问题，提高无人机的路径规划能力。
