Huang Yourui, Lu Sen, Liu Quanzeng, Han Tao, Li Tingting
School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China.
Anhui Polytechnic University, Wuhu 232000, China.
Biomimetics (Basel). 2025 Feb 6;10(2):92. doi: 10.3390/biomimetics10020092.
Aiming at the problem that the honey badger algorithm easily falls into local convergence, insufficient global search ability, and low convergence speed, this paper proposes a global optimization honey badger algorithm (Global Optimization HBA) (GOHBA), which improves the search ability of the population, with better ability to jump out of the local optimum, faster convergence speed, and better stability. The introduction of Tent chaotic mapping initialization enhances the population diversity and initializes the population quality of the HBA. Replacing the density factor enhances the search range of the algorithm in the entire solution space and avoids premature convergence to a local optimum. The addition of the golden sine strategy enhances the global search capability of the HBA and accelerates the convergence speed. Compared with seven algorithms, the GOHBA achieves the optimal mean value on 14 of the 23 tested functions. On two real-world engineering design problems, the GOHBA was optimal. On three path planning problems, the GOHBA had higher accuracy and faster convergence. The above experimental results show that the performance of the GOHBA is indeed excellent.
针对蜜獾算法容易陷入局部收敛、全局搜索能力不足和收敛速度慢的问题，本文提出了一种全局优化蜜獾算法（Global Optimization HBA）（GOHBA），该算法提高了种群的搜索能力，具有更好的跳出局部最优的能力、更快的收敛速度和更好的稳定性。引入帐篷混沌映射初始化增强了种群多样性并初始化了HBA的种群质量。替换密度因子扩大了算法在整个解空间中的搜索范围，避免过早收敛到局部最优。添加黄金正弦策略增强了HBA的全局搜索能力并加快了收敛速度。与七种算法相比，GOHBA在23个测试函数中的14个上实现了最优均值。在两个实际工程设计问题上，GOHBA是最优的。在三个路径规划问题上，GOHBA具有更高的精度和更快的收敛速度。上述实验结果表明GOHBA的性能确实优异。
