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Dung beetle optimizer: a new meta-heuristic algorithm for global optimization

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Abstract

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.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grants 61873059 and 61922024, and the Program of Shanghai Academic/Technology Research Leader under Grant 20XD1420100.

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Authors and Affiliations

  1. College of Information Science and Technology, Donghua University, Shanghai, China

    Jiankai Xue & Bo Shen

  2. Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai, China

    Jiankai Xue & Bo Shen

Authors
  1. Jiankai Xue
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  2. Bo Shen
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Contributions

JX contributed to conceptualization, methodology, software, investigation, writing-original draft. BS contributed to conceptualization, writing-review, editing, supervision, funding acquisition.

Corresponding author

Correspondence to Bo Shen.

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Xue, J., Shen, B. Dung beetle optimizer: a new meta-heuristic algorithm for global optimization. J Supercomput 79, 7305–7336 (2023). https://doi.org/10.1007/s11227-022-04959-6

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