Dung beetle optimizer based on mean fitness distance balance and multi-strategy fusion for solving practical engineering problems.

ABSTRACT: As a swarm intelligence algorithm, Dung beetle optimizer (DBO) was inspired by the behavior pattern of dung beetles for survival. This paper presents a dung beetle optimizer based on mean fitness distance balance and multi-strategy fusion (MMDBO), which addresses the slow convergence and weak global search of the original DBO. MMDBO improves performance by incorporating a cosine similarity strategy for position updates, enhancing convergence speed and diversity. The MFDB strategy is then applied to balance global exploration with local exploitation. Additionally, the Hypotrochoid and Levy flight strategies are used to enhance search ability and solution quality, while a non-uniform mutation strategy is introduced to avoid local optima. To comprehensively evaluate the optimizer performance

SUBMITTER: Tang W 

PROVIDER: S-EPMC12279994 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature