Multi-strategy dung beetle optimizer for global optimization and feature selection

Dung beetle optimizer (DBO) is a novel meta-heuristic algorithm proposed to imitate the habits of dung beetles. However, the parameter changes in the DBO affect the stability of the results. As the boundary shrunk is likely to cause overlap solutions, the algorithm eventually traps in local solutions. To overcome the weaknesses of DBO, the proposed version presents an integrated variant of DBO with the adaptive strategy, the dynamic boundaries individual position micro-adjustment strategy, and the mutation strategy, called BGADBO. First, an adaptive strategy is applied to overcome the instability caused by parameter changes. Then, introducing the linear scaling method to adjust the position of individuals within the dynamic boundary enriches the population diversity. The dynamic learning mechanism is introduced to enhance the adaptive capability of individuals when adjusting their positions. Finally, a Gaussian mutation mechanism is introduced to enhance the performance of the algorithm to escape the local optimum. In the experiment, we take the CEC2005 and CEC2019 benchmark functions to verify the performance of the proposed algorithm. In addition, the BGADBO is applied to several engineering optimization problems and feature selection (FS) problems to evaluate the application value. The experimental results indicate the proposed algorithm superior performance compared with the DBO and other well-established algorithms.