Multi-strategy Improved Dung Beetle Optimizer and Its Application
Dung beetle optimizer (DBO) is an intelligent optimization algorithm proposed in recent years. Like other optimization algorithms, DBO also has disadvantages such as low convergence accuracy and easy to fall into local optimum. A multi-strategy improved dung beetle optimizer (MIDBO) is proposed. Fir...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2024-04-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2308020.pdf |
| _version_ | 1797231005447225344 |
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| author | GUO Qin, ZHENG Qiaoxian |
| author_facet | GUO Qin, ZHENG Qiaoxian |
| author_sort | GUO Qin, ZHENG Qiaoxian |
| collection | DOAJ |
| description | Dung beetle optimizer (DBO) is an intelligent optimization algorithm proposed in recent years. Like other optimization algorithms, DBO also has disadvantages such as low convergence accuracy and easy to fall into local optimum. A multi-strategy improved dung beetle optimizer (MIDBO) is proposed. Firstly, it improves acceptance of local and global optimal solutions by brood balls and thieves, so that the beetles can dynamically change according to their own searching ability, which not only improves the population quality but also maintains the good searching ability of individuals with high fitness. Secondly, the follower position updating mechanism in the sparrow search algorithm is integrated to disturb the algorithm, and the greedy strategy is used to update the location, which improves the convergence accuracy of the algorithm. Finally, when the algorithm stagnates, Cauchy Gaussian variation strategy is introduced to improve the ability of the algorithm to jump out of the local optimal solution. Based on 20 benchmark test functions and CEC2019 test function, the simulation experiment verifies the effectiveness of the three improved strategies. The convergence analysis of the optimization results of the improved algorithm and the comparison algorithms and Wilcoxon rank sum test prove that MIDBO has good optimization performance and robustness. The validity and reliability of MIDBO in solving practical engineering problems are further verified by applying MIDBO to the solution of automobile collision optimization problems. |
| first_indexed | 2024-04-24T15:37:30Z |
| format | Article |
| id | doaj.art-8d8b8b696caf4d41abfb73985dd8ce36 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-04-24T15:37:30Z |
| publishDate | 2024-04-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-8d8b8b696caf4d41abfb73985dd8ce362024-04-02T01:27:22ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182024-04-0118493094610.3778/j.issn.1673-9418.2308020Multi-strategy Improved Dung Beetle Optimizer and Its ApplicationGUO Qin, ZHENG Qiaoxian01. School of Computer and Information Engineering，Hubei University, Wuhan 430062, China 2. School of Cyber Science and Technology, Hubei University, Wuhan 430062, ChinaDung beetle optimizer (DBO) is an intelligent optimization algorithm proposed in recent years. Like other optimization algorithms, DBO also has disadvantages such as low convergence accuracy and easy to fall into local optimum. A multi-strategy improved dung beetle optimizer (MIDBO) is proposed. Firstly, it improves acceptance of local and global optimal solutions by brood balls and thieves, so that the beetles can dynamically change according to their own searching ability, which not only improves the population quality but also maintains the good searching ability of individuals with high fitness. Secondly, the follower position updating mechanism in the sparrow search algorithm is integrated to disturb the algorithm, and the greedy strategy is used to update the location, which improves the convergence accuracy of the algorithm. Finally, when the algorithm stagnates, Cauchy Gaussian variation strategy is introduced to improve the ability of the algorithm to jump out of the local optimal solution. Based on 20 benchmark test functions and CEC2019 test function, the simulation experiment verifies the effectiveness of the three improved strategies. The convergence analysis of the optimization results of the improved algorithm and the comparison algorithms and Wilcoxon rank sum test prove that MIDBO has good optimization performance and robustness. The validity and reliability of MIDBO in solving practical engineering problems are further verified by applying MIDBO to the solution of automobile collision optimization problems.http://fcst.ceaj.org/fileup/1673-9418/PDF/2308020.pdfdung beetle optimization algorithm; local optimal solution; sparrow search algorithm; cauchy gaussian variation; car collision optimization problems; wilcoxon rank sum test |
| spellingShingle | GUO Qin, ZHENG Qiaoxian Multi-strategy Improved Dung Beetle Optimizer and Its Application Jisuanji kexue yu tansuo dung beetle optimization algorithm; local optimal solution; sparrow search algorithm; cauchy gaussian variation; car collision optimization problems; wilcoxon rank sum test |
| title | Multi-strategy Improved Dung Beetle Optimizer and Its Application |
| title_full | Multi-strategy Improved Dung Beetle Optimizer and Its Application |
| title_fullStr | Multi-strategy Improved Dung Beetle Optimizer and Its Application |
| title_full_unstemmed | Multi-strategy Improved Dung Beetle Optimizer and Its Application |
| title_short | Multi-strategy Improved Dung Beetle Optimizer and Its Application |
| title_sort | multi strategy improved dung beetle optimizer and its application |
| topic | dung beetle optimization algorithm; local optimal solution; sparrow search algorithm; cauchy gaussian variation; car collision optimization problems; wilcoxon rank sum test |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2308020.pdf |
| work_keys_str_mv | AT guoqinzhengqiaoxian multistrategyimproveddungbeetleoptimizeranditsapplication |