Balanced dung beetle optimization algorithm based on parameter substitution and escape strategy
Abstract Dung Beetle algorithm is an intelligent optimization algorithm with advantages in exploitation ability. However, due to the high randomness of parameters, premature convergence and other reasons, there is an imbalance between exploration and exploitation ability, and it is easy to fall into...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86264-9 |
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| author | Chen-Xu Tian Yu-Xuan Li |
| author_facet | Chen-Xu Tian Yu-Xuan Li |
| author_sort | Chen-Xu Tian |
| collection | DOAJ |
| description | Abstract Dung Beetle algorithm is an intelligent optimization algorithm with advantages in exploitation ability. However, due to the high randomness of parameters, premature convergence and other reasons, there is an imbalance between exploration and exploitation ability, and it is easy to fall into the problem of local optimal solution. The purpose of this study is to improve the optimization performance of dung beetle algorithm and explore its engineering application value. A balanced dung beetle optimization algorithm was proposed, and parabolic adaptive parameter $$R$$ R was introduced to broaden the exploration range and slow down premature convergence. Gaussian distributed phase parameter $$\beta$$ β is introduced to reduce the randomness of parameters and stimulate the potential of algorithm exploitation. Levy flight escape strategy is introduced to balance the global exploration ability of the algorithm and fully explore the solution space. The effectiveness of the improved strategy is verified by comparing the CEC2017 benchmark function with the single strategy variant. The experimental results show that BDBO algorithm is superior to other algorithms in terms of convergence accuracy and generalization ability, and the accuracy improvement percentage is 35.29% compared with DBO algorithm. Wilcoxon rank sum test was used to evaluate the experimental results, which proved that the improvement strategy was statistically significant. Finally, the BDBO algorithm is applied to the tracking technology of the maximum power point of the photovoltaic system, and the experimental results show that the application effect of the BDBO algorithm is better and has more engineering application value. |
| format | Article |
| id | doaj-art-095bd281ace5455583bedecabba89597 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-095bd281ace5455583bedecabba895972025-01-19T12:17:32ZengNature PortfolioScientific Reports2045-23222025-01-0115112910.1038/s41598-025-86264-9Balanced dung beetle optimization algorithm based on parameter substitution and escape strategyChen-Xu Tian0Yu-Xuan Li1School of Statistics and Applied Mathematics, Anhui University of Finance and EconomicsSchool of Statistics and Applied Mathematics, Anhui University of Finance and EconomicsAbstract Dung Beetle algorithm is an intelligent optimization algorithm with advantages in exploitation ability. However, due to the high randomness of parameters, premature convergence and other reasons, there is an imbalance between exploration and exploitation ability, and it is easy to fall into the problem of local optimal solution. The purpose of this study is to improve the optimization performance of dung beetle algorithm and explore its engineering application value. A balanced dung beetle optimization algorithm was proposed, and parabolic adaptive parameter $$R$$ R was introduced to broaden the exploration range and slow down premature convergence. Gaussian distributed phase parameter $$\beta$$ β is introduced to reduce the randomness of parameters and stimulate the potential of algorithm exploitation. Levy flight escape strategy is introduced to balance the global exploration ability of the algorithm and fully explore the solution space. The effectiveness of the improved strategy is verified by comparing the CEC2017 benchmark function with the single strategy variant. The experimental results show that BDBO algorithm is superior to other algorithms in terms of convergence accuracy and generalization ability, and the accuracy improvement percentage is 35.29% compared with DBO algorithm. Wilcoxon rank sum test was used to evaluate the experimental results, which proved that the improvement strategy was statistically significant. Finally, the BDBO algorithm is applied to the tracking technology of the maximum power point of the photovoltaic system, and the experimental results show that the application effect of the BDBO algorithm is better and has more engineering application value.https://doi.org/10.1038/s41598-025-86264-9Dung beetle optimization algorithmSwarm intelligence optimization algorithmParameter substitutionExploitation potentialEscape strategyMPPT |
| spellingShingle | Chen-Xu Tian Yu-Xuan Li Balanced dung beetle optimization algorithm based on parameter substitution and escape strategy Scientific Reports Dung beetle optimization algorithm Swarm intelligence optimization algorithm Parameter substitution Exploitation potential Escape strategy MPPT |
| title | Balanced dung beetle optimization algorithm based on parameter substitution and escape strategy |
| title_full | Balanced dung beetle optimization algorithm based on parameter substitution and escape strategy |
| title_fullStr | Balanced dung beetle optimization algorithm based on parameter substitution and escape strategy |
| title_full_unstemmed | Balanced dung beetle optimization algorithm based on parameter substitution and escape strategy |
| title_short | Balanced dung beetle optimization algorithm based on parameter substitution and escape strategy |
| title_sort | balanced dung beetle optimization algorithm based on parameter substitution and escape strategy |
| topic | Dung beetle optimization algorithm Swarm intelligence optimization algorithm Parameter substitution Exploitation potential Escape strategy MPPT |
| url | https://doi.org/10.1038/s41598-025-86264-9 |
| work_keys_str_mv | AT chenxutian balanceddungbeetleoptimizationalgorithmbasedonparametersubstitutionandescapestrategy AT yuxuanli balanceddungbeetleoptimizationalgorithmbasedonparametersubstitutionandescapestrategy |