An Improved Human Evolution Optimization Algorithm for Unmanned Aerial Vehicle 3D Trajectory Planning
To address the challenges of slow convergence speed, poor convergence precision, and getting stuck in local optima for unmanned aerial vehicle (UAV) three-dimensional path planning, this paper proposes a path planning method based on an Improved Human Evolution Optimization Algorithm (IHEOA). First,...
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MDPI AG
2025-01-01
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| author | Xue Wang Shiyuan Zhou Zijia Wang Xiaoyun Xia Yaolong Duan |
| author_facet | Xue Wang Shiyuan Zhou Zijia Wang Xiaoyun Xia Yaolong Duan |
| author_sort | Xue Wang |
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| description | To address the challenges of slow convergence speed, poor convergence precision, and getting stuck in local optima for unmanned aerial vehicle (UAV) three-dimensional path planning, this paper proposes a path planning method based on an Improved Human Evolution Optimization Algorithm (IHEOA). First, a mathematical model is used to construct a three-dimensional terrain environment, and a multi-constraint path cost model is established, framing path planning as a multidimensional function optimization problem. Second, recognizing the sensitivity of population diversity to Logistic Chaotic Mapping in a traditional Human Evolution Optimization Algorithm (HEOA), an opposition-based learning strategy is employed to uniformly initialize the population distribution, thereby enhancing the algorithm’s global optimization capability. Additionally, a guidance factor strategy is introduced into the leader role during the development stage, providing clear directionality for the search process, which increases the probability of selecting optimal paths and accelerates the convergence speed. Furthermore, in the loser update strategy, an adaptive <i>t</i>-distribution perturbation strategy is utilized for its small mutation amplitude, which enhances the local search capability and robustness of the algorithm. Evaluations using 12 standard test functions demonstrate that these improvement strategies effectively enhance convergence precision and algorithm stability, with the IHEOA, which integrates multiple strategies, performing particularly well. Experimental comparative research on three different terrain environments and five traditional algorithms shows that the IHEOA not only exhibits excellent performance in terms of convergence speed and precision but also generates superior paths while demonstrating exceptional global optimization capability and robustness in complex environments. These results validate the significant advantages of the proposed improved algorithm in effectively addressing UAV path planning challenges. |
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| id | doaj-art-1478e27bfe4743548c15026641b56614 |
| institution | Kabale University |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-1478e27bfe4743548c15026641b566142025-01-24T13:24:38ZengMDPI AGBiomimetics2313-76732025-01-011012310.3390/biomimetics10010023An Improved Human Evolution Optimization Algorithm for Unmanned Aerial Vehicle 3D Trajectory PlanningXue Wang0Shiyuan Zhou1Zijia Wang2Xiaoyun Xia3Yaolong Duan4School of Artificial Intelligence, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Information Engineering, Jiaxing Nanhu University, Jiaxing 314001, ChinaSchool of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, ChinaSchool of Artificial Intelligence, Jiaxing University, Jiaxing 314001, ChinaTechnology Research and Development Centre, Xuelong Group Co., Ltd., Ningbo 315899, ChinaTo address the challenges of slow convergence speed, poor convergence precision, and getting stuck in local optima for unmanned aerial vehicle (UAV) three-dimensional path planning, this paper proposes a path planning method based on an Improved Human Evolution Optimization Algorithm (IHEOA). First, a mathematical model is used to construct a three-dimensional terrain environment, and a multi-constraint path cost model is established, framing path planning as a multidimensional function optimization problem. Second, recognizing the sensitivity of population diversity to Logistic Chaotic Mapping in a traditional Human Evolution Optimization Algorithm (HEOA), an opposition-based learning strategy is employed to uniformly initialize the population distribution, thereby enhancing the algorithm’s global optimization capability. Additionally, a guidance factor strategy is introduced into the leader role during the development stage, providing clear directionality for the search process, which increases the probability of selecting optimal paths and accelerates the convergence speed. Furthermore, in the loser update strategy, an adaptive <i>t</i>-distribution perturbation strategy is utilized for its small mutation amplitude, which enhances the local search capability and robustness of the algorithm. Evaluations using 12 standard test functions demonstrate that these improvement strategies effectively enhance convergence precision and algorithm stability, with the IHEOA, which integrates multiple strategies, performing particularly well. Experimental comparative research on three different terrain environments and five traditional algorithms shows that the IHEOA not only exhibits excellent performance in terms of convergence speed and precision but also generates superior paths while demonstrating exceptional global optimization capability and robustness in complex environments. These results validate the significant advantages of the proposed improved algorithm in effectively addressing UAV path planning challenges.https://www.mdpi.com/2313-7673/10/1/23improved human evolution optimization algorithmlogistic chaotic mappingopposition-based learning strategyguidance factoradaptive <i>t</i>-distribution perturbation |
| spellingShingle | Xue Wang Shiyuan Zhou Zijia Wang Xiaoyun Xia Yaolong Duan An Improved Human Evolution Optimization Algorithm for Unmanned Aerial Vehicle 3D Trajectory Planning Biomimetics improved human evolution optimization algorithm logistic chaotic mapping opposition-based learning strategy guidance factor adaptive <i>t</i>-distribution perturbation |
| title | An Improved Human Evolution Optimization Algorithm for Unmanned Aerial Vehicle 3D Trajectory Planning |
| title_full | An Improved Human Evolution Optimization Algorithm for Unmanned Aerial Vehicle 3D Trajectory Planning |
| title_fullStr | An Improved Human Evolution Optimization Algorithm for Unmanned Aerial Vehicle 3D Trajectory Planning |
| title_full_unstemmed | An Improved Human Evolution Optimization Algorithm for Unmanned Aerial Vehicle 3D Trajectory Planning |
| title_short | An Improved Human Evolution Optimization Algorithm for Unmanned Aerial Vehicle 3D Trajectory Planning |
| title_sort | improved human evolution optimization algorithm for unmanned aerial vehicle 3d trajectory planning |
| topic | improved human evolution optimization algorithm logistic chaotic mapping opposition-based learning strategy guidance factor adaptive <i>t</i>-distribution perturbation |
| url | https://www.mdpi.com/2313-7673/10/1/23 |
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