A Novel Honey Badger Algorithm with Chaotic and Adaptive Strategies for Engineering Problem

In order to improve the optimization performance of Honey Badger algorithm, this study proposes an enhanced Honey Badger Algorithm (EHBA) based on multi-strategy fusion of chaotic initialization, Gaussian mutation and mirror learning. Circle mapping was adopted to enhance the ergodicity of the initialized population; A Gaussian mutation strategy is designed to avoid falling into local optimum. Furthermore, a mirror learning strategy was proposed to improve the global search ability of the EHBA. The results compared with a variety of advanced algorithms show that the proposed EHBA has advantages such as high convergence accuracy and good stability. Besides, the effectiveness of the EHBA has been verified in practical engineering problems.