Symbiotic mechanism-based honey badger algorithm for continuous optimization

Honey badger algorithm (HBA) is a novel meta-heuristic algorithm inspired by the foraging behaviors of honey badgers, designed to tackle single-objective continuous optimization problems. While the standard HBA demonstrates strong performance across various optimization problems, it still encounters challenges related to insufficient population diversity, particularly in later stages, hindering its ability to escape local optima. To address these problems, we propose an enhanced variant called the symbiotic mechanism-based HBA (SHBA), which incorporates the cooperative symbiotic mechanism between honey badgers and honey-guide birds. Specifically, it integrates information exchange between two distinct populations and employs multiple strategies to increase population diversity while maintaining efficient search performance. We conducted numerical experiments on CEC2017 and CEC2022 and compared their performance with nine competitive algorithms. Furthermore, the scalability and applicability of the proposed SHBA were assessed across various engineering problems. The statistical results show that the proposed SHBA is significantly competitive, with excellent convergence speed and accuracy in benchmark functions and engineering problems.