Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Aug 29;9(9):517.
doi: 10.3390/biomimetics9090517.

Research on Move-to-Escape Enhanced Dung Beetle Optimization and Its Applications

Shuwan Feng  1 , Jihong Wang  2 , Ziming Li  3 , Sai Wang  4 , Ziyi Cheng  5 , Hui Yu  6 , Jiasheng Zhong  7
Affiliations

Research on Move-to-Escape Enhanced Dung Beetle Optimization and Its Applications

Shuwan Feng et al. Biomimetics (Basel). .

Abstract

The dung beetle optimization (DBO) algorithm is acknowledged for its robust optimization capabilities and rapid convergence as an efficient swarm intelligence optimization technique. Nevertheless, DBO, similar to other swarm intelligence algorithms, often gets trapped in local optima during the later stages of optimization. To mitigate this challenge, we propose the Move-to-Escape dung beetle optimization (MEDBO) algorithm in this paper. MEDBO utilizes a good point set strategy for initializing the swarm's initial population, ensuring a more uniform distribution and diminishing the risk of local optima entrapment. Moreover, it incorporates convergence factors and dynamically balances the number of offspring and foraging individuals, prioritizing global exploration initially and local exploration subsequently. This dynamic adjustment not only enhances the search speed but also prevents local optima stagnation. The algorithm's performance was assessed using the CEC2017 benchmark suite, which confirmed MEDBO's significant improvements. Additionally, we applied MEDBO to three engineering problems: pressure vessel design, three-bar truss design, and spring design. MEDBO exhibited an excellent performance in these applications, demonstrating its practicality and efficacy in real-world problem-solving contexts.

Keywords: DBO; combinatorial optimization; discrete optimization; evolutionary algorithms; optimization algorithms.

PubMed Disclaimer

Conflict of interest statement

Author Yujun Zhao was employed by the company Jinan Lingong Mining and Rock Technology Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. This research was conducted in Manchester.

Figures

Figure 1
Figure 1
The value of tan(x).
Figure 2
Figure 2
The components of the dung beetle’s foraging behavior.
Figure 3
Figure 3
The optimal path combining the walk function.
Figure 4
Figure 4
CEC2017 test curves chart (Dim = 30).
Figure 5
Figure 5
CEC2017 test curves chart (Dim = 100).
Figure 6
Figure 6
The convergence curves by the DBO algorithm and other optimizers on benchmark test functions (Dim = 30).
Figure 6
Figure 6
The convergence curves by the DBO algorithm and other optimizers on benchmark test functions (Dim = 30).
Figure 7
Figure 7
The convergence curves by the DBO algorithm and other optimizers on benchmark test functions (Dim = 100).
Figure 8
Figure 8
Extension (compression) spring-design issues.
Figure 9
Figure 9
The tension (compression) spring-design convergence plot.
Figure 10
Figure 10
Triangle truss design.
Figure 11
Figure 11
Three-bar truss convergence curve diagram.
Figure 12
Figure 12
Pressure vessel parameter diagram.
Figure 13
Figure 13
The iteration plot of the pressure vessel problem.
See this image and copyright information in PMC

References

    1. Dorigo M. Swarm Intell. Universit’e Libre de Bruxelles; Brussels, Belgium: 2016. Swarm intelligence: A few things you need to know if you want to publish in this journal.
    1. Kennedy J., Eberhart R. Particle swarm optimization; Proceedings of the ICNN’95-International Conference on Neural Networks; Perth, WA, Australia. 27 November–1 December 1995; Piscataway, NJ, USA: IEEE; 1995. pp. 1942–1948.
    1. Heidari A.A., Mirjalili S., Faris H., Aljarah I., Mafarja M., Chen H. Harris hawks optimization: Algorithm and applications. Future Gener. Comput. Syst. 2019;97:849–872. doi: 10.1016/j.future.2019.02.028. - DOI
    1. Mirjalili S. Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 2016;27:1053–1073. doi: 10.1007/s00521-015-1920-1. - DOI
    1. Xue J., Shen B. Dung beetle optimizer: A new meta-heuristic algorithm for global optimization. J. Supercomput. 2023;79:7305–7336. doi: 10.1007/s11227-022-04959-6. - DOI

LinkOut - more resources