Improvement of the Honey Badger Algorithm and Its Application in Engineering Problems Research

The Honey Badger Algorithm (HBA) is a novel swarm intelligence optimization algorithm that simulates the foraging behavior of honey badgers in the wild. This algorithm achieves optimization primarily by mimicking the foraging behavior of honey badgers, exhibiting strong optimization ability and a fast convergence speed. However, it also has problems, such as a limited search ability and a tendency to become stuck in local optima. Based on this, an improved honey badger optimization algorithm is proposed in this paper to enhance the performance of the honey badger algorithm. Firstly, the good point set strategy is adopted for initialization to distribute the positions of individuals evenly. Secondly, elementary functions are used to replace the density factor, thereby improving search efficiency and accuracy. Then, in the mining stage, the original update strategy and the whale spiral search strategy are employed to increase the robustness of the algorithm. Finally, the lens imaging reverse learning strategy is adopted in the update strategy to improve the performance of the original algorithm comprehensively. To verify the effectiveness of the improved honey badger algorithm, it is compared with five well-known meta-heuristic algorithms in terms of the test results on the CEC2021 benchmark test set. Subsequently, the performance advantages of the improved honey badger algorithm are verified through two engineering design problems. The simulation results show that the improved honey badger algorithm has better robustness and faster convergence speed.