DM-HBA: Dynamic mean-based honey badger algorithm for engineering design optimization problems

Abstract

Abstract The honey badger algorithm (HBA) has attracted the interest of researchers from various fields for solving optimization problems. However, it is prone to rapid loss of diversity and imbalanced exploration-exploitation behavior, leading to premature convergence in local optima and limiting final solution accuracy. To overcome these drawbacks, this paper proposes a novel dynamic mean-based HBA (DM-HBA) with two enhancements: (1) a mean-based guidance strategy that maintains population diversity and allows the algorithm to escape local optima. (2) A dynamic attraction parameter enables a smooth transition from exploration to exploitation. Finally, 41 benchmark functions from the CEC’17 and CEC’22 test suites, as well as three complex real-world engineering problems, are used. Statistical analyses revealed that DM-HBA outperformed HBA on 72% of the functions, with mean improvements of 21-25% in best-fitness values and approximately 26-34% reductions in standard deviation across 30D, 50D and 100D. For the more challenging CEC’22 suite, at 20D, it outperforms HBA on 75% of the functions, with a mean improvement of 8.7% and an approximately 31% standard deviation reduction, demonstrating that DM-HBA is both robust and scalable, with a well-balanced exploration-exploitation process. The source code is available at GitHub.