Enhanced honey badger algorithm based on nonlinear adaptive weight and golden sine operator

The honey badger algorithm (HBA) is a swarm intelligence algorithm that imitates honey badgers’ intelligent foraging techniques. HBA diversifies and intensifies the search space by simulating digging and honey-finding strategies. However, HBA suffers from slow convergence speed, imbalanced diversification, and intensification problems. Therefore, we developed the nonlinear adaptive weight and the golden sine operator-based enhanced HBA (NGS-eHBA). The newly added nonlinear adaptive weight explores the search space adaptively, balancing its diversification and intensification. Next, we incorporate the improved golden sine operator to establish a sine route that accelerates the global convergence speed during the search. We compare NGS-eHBA with recent optimization algorithms using well-known benchmark functions for performance evaluation, and statistical analyses show that it outperforms other algorithms. We also use the NGS-eHBA algorithm to resolve engineering design problems, where it outperforms other algorithms noticeably.