Centroid Opposite Honey Badger Algorithm Based on Adaptive T-Distribution Variation

Recently, the field of intelligent optimization has attracted many scholars to propose and develop various Swarm intelligent algorithms. In order to better apply swarm intelligence algorithms to practical problems, we still need to improve them to have better global exploration and convergence capabilities. In this paper, the Honey Badger Algorithm (HBA) is improved to make significant improvements in both convergence and exploration capabilities, and the improved algorithm is named Centroid Opposite honey badger algorithm based on adaptive t-distribution variation (CTHBA). In the improvement process, the efficient convergence capability of Centroid opposition-based computation is first utilized to enhance the convergence accuracy and convergence speed of HBA. The t-distribution variation is added at the end of the iteration to perturb the honey badger individuals in the dominant population to make it more balanced between exploration and exploitation. The performance of the improved algorithm is evaluated through the CEC2021 dataset, which well verifies the excellence of CTHBA compared to other algorithms.