Dung beetle optimizer: a new meta‑heuristic algorithm

Xue Jiankai, et al.Dung beetle optimizer: a new meta‑heuristic algorithm for global optimization, https://doi.org/10.1007/s11227-022-04959-6

In this paper, a novel population-based technique called dung beetle optimizer

(DBO) algorithm is presented, which is inspired by the ball-rolling, dancing, foraging,

stealing, and reproduction behaviors of dung beetles. The newly proposed DBO

algorithm takes into account both the global exploration and the local exploitation,

thereby having the characteristics of the fast convergence rate and the satisfactory

solution accuracy. A series of well-known mathematical test functions (including

both 23 benchmark functions and 29 CEC-BC-2017 test functions) are employed to

evaluate the search capability of the DBO algorithm. From the simulation results, it

is observed that the DBO algorithm presents substantially competitive performance

with the state-of-the-art optimization approaches in terms of the convergence rate,

solution accuracy, and stability. In addition, the Wilcoxon signed-rank test and the

Friedman test are used to evaluate the experimental results of the algorithms, which

proves the superiority of the DBO algorithm against other currently popular optimization

techniques. In order to further illustrate the practical application potential, the

DBO algorithm is successfully applied in three engineering design problems. The

experimental results demonstrate that the proposed DBO algorithm can effectively

deal with real-world application problems.