Dung beetle optimizer: a new meta‑heuristic algorithm

版本 1.0.0 (3.6 MB) 作者: Dream
Xue Jiankai, et al.Dung beetle optimizer: a new meta‑heuristic algorithm for global optimization, https://doi.org/10.1007/s11227-022-04959-6
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更新时间 2022/11/29

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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.

引用格式

Xue, Jiankai, and Bo Shen. “Dung Beetle Optimizer: a New Meta-Heuristic Algorithm for Global Optimization.” The Journal of Supercomputing, Springer Science and Business Media LLC, Nov. 2022, doi:10.1007/s11227-022-04959-6.

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DBO

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1.0.0