GOHBA: Improved Honey Badger Algorithm for Global Optimization

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.