An enhanced dung beetle optimizer with multiple strategies for robot path planning
Abstract In order to make up for the shortcomings of the original dung beetle optimization algorithm, such as low population diversity, insufficient of global exploration ability, being easy to fall into local optimization and unsatisfactory convergence accuracy, etc. An improved dung beetle optimiz...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88347-z |
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| author | Wei Hu Qi Zhang Shan Ye |
| author_facet | Wei Hu Qi Zhang Shan Ye |
| author_sort | Wei Hu |
| collection | DOAJ |
| description | Abstract In order to make up for the shortcomings of the original dung beetle optimization algorithm, such as low population diversity, insufficient of global exploration ability, being easy to fall into local optimization and unsatisfactory convergence accuracy, etc. An improved dung beetle optimization algorithm using hybrid multi- strategy is proposed. Firstly, the cubic chaotic mapping approach is used to initialize the population to improve the diversity, expand the search range of the solution space, and enhance the global optimization ability. Secondly, the cooperative search algorithm is utilized to strength communication between individual dung beetles and dung beetle groups in foraging stage to expand the search range of the solution space and enhance the global optimization ability. Thirdly, T-distribution mutation and differential evolutionary variation strategies are introduced to provide perturbation to enhance the diversity of the population and avoid falling into local optimization. Fourthly, the proposed algorithm(named as SSTDBO) is compared with other optimization algorithms, including GODBO, QHDBO, DBO, KOA, NOA, WOA and HHO, by 29 benchmark test functions in CEC2017. The results show that the proposed algorithm has stronger robustness and optimization ability, and algorithm’s performance has substantially enhanced. Finally, the proposed algorithm is applied to solve the real-world robot path planning engineering cases, to demonstrate its effectiveness in dealing with real optimization engineering cases, which further verified how noteworthy the enhanced strategy’s efficacy and the enhanced algorithm’s superiority are in addressing real-world engineering cases. |
| format | Article |
| id | doaj-art-c938ffe888ed4e5b8a3738f2e492e226 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c938ffe888ed4e5b8a3738f2e492e2262025-02-09T12:28:32ZengNature PortfolioScientific Reports2045-23222025-02-0115113410.1038/s41598-025-88347-zAn enhanced dung beetle optimizer with multiple strategies for robot path planningWei Hu0Qi Zhang1Shan Ye2Panzhihua UniversityChengdu Technological UniversityPanzhihua UniversityAbstract In order to make up for the shortcomings of the original dung beetle optimization algorithm, such as low population diversity, insufficient of global exploration ability, being easy to fall into local optimization and unsatisfactory convergence accuracy, etc. An improved dung beetle optimization algorithm using hybrid multi- strategy is proposed. Firstly, the cubic chaotic mapping approach is used to initialize the population to improve the diversity, expand the search range of the solution space, and enhance the global optimization ability. Secondly, the cooperative search algorithm is utilized to strength communication between individual dung beetles and dung beetle groups in foraging stage to expand the search range of the solution space and enhance the global optimization ability. Thirdly, T-distribution mutation and differential evolutionary variation strategies are introduced to provide perturbation to enhance the diversity of the population and avoid falling into local optimization. Fourthly, the proposed algorithm(named as SSTDBO) is compared with other optimization algorithms, including GODBO, QHDBO, DBO, KOA, NOA, WOA and HHO, by 29 benchmark test functions in CEC2017. The results show that the proposed algorithm has stronger robustness and optimization ability, and algorithm’s performance has substantially enhanced. Finally, the proposed algorithm is applied to solve the real-world robot path planning engineering cases, to demonstrate its effectiveness in dealing with real optimization engineering cases, which further verified how noteworthy the enhanced strategy’s efficacy and the enhanced algorithm’s superiority are in addressing real-world engineering cases.https://doi.org/10.1038/s41598-025-88347-zDung Beetle OptimizerChaotic mapping strategyCooperative Search AlgorithmT-Distribution variation strategiesDifferential Evolutionary variation strategiesCEC2017 |
| spellingShingle | Wei Hu Qi Zhang Shan Ye An enhanced dung beetle optimizer with multiple strategies for robot path planning Scientific Reports Dung Beetle Optimizer Chaotic mapping strategy Cooperative Search Algorithm T-Distribution variation strategies Differential Evolutionary variation strategies CEC2017 |
| title | An enhanced dung beetle optimizer with multiple strategies for robot path planning |
| title_full | An enhanced dung beetle optimizer with multiple strategies for robot path planning |
| title_fullStr | An enhanced dung beetle optimizer with multiple strategies for robot path planning |
| title_full_unstemmed | An enhanced dung beetle optimizer with multiple strategies for robot path planning |
| title_short | An enhanced dung beetle optimizer with multiple strategies for robot path planning |
| title_sort | enhanced dung beetle optimizer with multiple strategies for robot path planning |
| topic | Dung Beetle Optimizer Chaotic mapping strategy Cooperative Search Algorithm T-Distribution variation strategies Differential Evolutionary variation strategies CEC2017 |
| url | https://doi.org/10.1038/s41598-025-88347-z |
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