A Novel Improved Dung Beetle Optimization Algorithm for Collaborative 3D Path Planning of UAVs
In this study, we propose a novel improved Dung Beetle Optimizer called Environment-aware Chaotic Force-field Dung Beetle Optimizer (ECFDBO). To address DBO’s existing tendency toward premature convergence and insufficient precision in high-dimensional, complex search spaces, ECFDBO integrates three...
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MDPI AG
2025-06-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/7/420 |
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| author | Xiaojun Zheng Rundong Liu Siyang Li |
| author_facet | Xiaojun Zheng Rundong Liu Siyang Li |
| author_sort | Xiaojun Zheng |
| collection | DOAJ |
| description | In this study, we propose a novel improved Dung Beetle Optimizer called Environment-aware Chaotic Force-field Dung Beetle Optimizer (ECFDBO). To address DBO’s existing tendency toward premature convergence and insufficient precision in high-dimensional, complex search spaces, ECFDBO integrates three key improvements: a chaotic perturbation-based nonlinear contraction strategy, an intelligent boundary-handling mechanism, and a dynamic attraction–repulsion force-field mutation. These improvements reinforce both the algorithm’s global exploration capability and its local exploitation accuracy. We conducted 30 independent runs of ECFDBO on the CEC2017 benchmark suite. Compared with seven classical and novel metaheuristic algorithms, ECFDBO achieved statistically significant improvements in multiple performance metrics. Moreover, by varying problem dimensionality, we demonstrated its robust global optimization capability for increasingly challenging tasks. We further conducted the Wilcoxon and Friedman tests to assess the significance of performance differences of the algorithms and to establish an overall ranking. Finally, ECFDBO was applied to a 3D path planning simulation in UAVs for safe path planning in complex environments. Against both the Dung Beetle Optimizer and a multi-strategy DBO (GODBO) algorithm, ECFDBO met the global optimality requirements for cooperative UAV planning and showed strong potential for high-dimensional global optimization applications. |
| format | Article |
| id | doaj-art-461413305e7d4878a315c604a284d345 |
| institution | Kabale University |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-06-01 |
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| series | Biomimetics |
| spelling | doaj-art-461413305e7d4878a315c604a284d3452025-08-20T03:58:30ZengMDPI AGBiomimetics2313-76732025-06-0110742010.3390/biomimetics10070420A Novel Improved Dung Beetle Optimization Algorithm for Collaborative 3D Path Planning of UAVsXiaojun Zheng0Rundong Liu1Siyang Li2School of Mechanical Engineering, Dalian Jiaotong University, Dalian 116028, ChinaSchool of Mechanical Engineering, Dalian Jiaotong University, Dalian 116028, ChinaSchool of Mechanical Engineering, Dalian Jiaotong University, Dalian 116028, ChinaIn this study, we propose a novel improved Dung Beetle Optimizer called Environment-aware Chaotic Force-field Dung Beetle Optimizer (ECFDBO). To address DBO’s existing tendency toward premature convergence and insufficient precision in high-dimensional, complex search spaces, ECFDBO integrates three key improvements: a chaotic perturbation-based nonlinear contraction strategy, an intelligent boundary-handling mechanism, and a dynamic attraction–repulsion force-field mutation. These improvements reinforce both the algorithm’s global exploration capability and its local exploitation accuracy. We conducted 30 independent runs of ECFDBO on the CEC2017 benchmark suite. Compared with seven classical and novel metaheuristic algorithms, ECFDBO achieved statistically significant improvements in multiple performance metrics. Moreover, by varying problem dimensionality, we demonstrated its robust global optimization capability for increasingly challenging tasks. We further conducted the Wilcoxon and Friedman tests to assess the significance of performance differences of the algorithms and to establish an overall ranking. Finally, ECFDBO was applied to a 3D path planning simulation in UAVs for safe path planning in complex environments. Against both the Dung Beetle Optimizer and a multi-strategy DBO (GODBO) algorithm, ECFDBO met the global optimality requirements for cooperative UAV planning and showed strong potential for high-dimensional global optimization applications.https://www.mdpi.com/2313-7673/10/7/420dung beetle optimizerimprovement strategiesCEC2017path planningcooperative UAVs |
| spellingShingle | Xiaojun Zheng Rundong Liu Siyang Li A Novel Improved Dung Beetle Optimization Algorithm for Collaborative 3D Path Planning of UAVs Biomimetics dung beetle optimizer improvement strategies CEC2017 path planning cooperative UAVs |
| title | A Novel Improved Dung Beetle Optimization Algorithm for Collaborative 3D Path Planning of UAVs |
| title_full | A Novel Improved Dung Beetle Optimization Algorithm for Collaborative 3D Path Planning of UAVs |
| title_fullStr | A Novel Improved Dung Beetle Optimization Algorithm for Collaborative 3D Path Planning of UAVs |
| title_full_unstemmed | A Novel Improved Dung Beetle Optimization Algorithm for Collaborative 3D Path Planning of UAVs |
| title_short | A Novel Improved Dung Beetle Optimization Algorithm for Collaborative 3D Path Planning of UAVs |
| title_sort | novel improved dung beetle optimization algorithm for collaborative 3d path planning of uavs |
| topic | dung beetle optimizer improvement strategies CEC2017 path planning cooperative UAVs |
| url | https://www.mdpi.com/2313-7673/10/7/420 |
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