An Adaptive Evolutionary Multi-Objective Estimation of Distribution Algorithm and Its Application to Multi-UAV Path Planning
This paper concerns the multi-UAV cooperative path planning problem, which is solved by multi-objective optimization and by an adaptive evolutionary multi-objective estimation of distribution algorithm (AEMO-EDA). Since the traditional multi-objective optimization algorithms tend to fall into local...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10108001/ |
| _version_ | 1797817100676366336 |
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| author | Ren Yuhang Zhang Liang |
| author_facet | Ren Yuhang Zhang Liang |
| author_sort | Ren Yuhang |
| collection | DOAJ |
| description | This paper concerns the multi-UAV cooperative path planning problem, which is solved by multi-objective optimization and by an adaptive evolutionary multi-objective estimation of distribution algorithm (AEMO-EDA). Since the traditional multi-objective optimization algorithms tend to fall into local optimum solutions when dealing with optimization problems in three dimensions, we suggest an advanced estimation of distribution algorithm. The main idea of this algorithm is to integrate the adaptive deflation of the selection rate, adaptive evolution of the covariance matrix, comprehensive evaluation of individual convergence and diversity, and reference point-based non-dominated ranking. A multi-UAV path planning model involving multi-objective optimization is established, and the designed algorithm is simulated and compared with other three high-dimensional multi-objective optimization algorithms. The results show that the AEMO-EDA proposed in this paper has stronger convergence and wider population distribution diversity in applying to the multi-UAV cooperative path planning model, as well as better global convergence. The algorithm can provide an stable path for each UAV and promote the intelligent operation of the UAV system. |
| first_indexed | 2024-03-13T08:47:03Z |
| format | Article |
| id | doaj.art-6a8d124998684d508574717d052f8e85 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T08:47:03Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6a8d124998684d508574717d052f8e852023-05-29T23:00:20ZengIEEEIEEE Access2169-35362023-01-0111500385005110.1109/ACCESS.2023.327029710108001An Adaptive Evolutionary Multi-Objective Estimation of Distribution Algorithm and Its Application to Multi-UAV Path PlanningRen Yuhang0https://orcid.org/0000-0002-6182-4666Zhang Liang1https://orcid.org/0000-0001-6584-5440Department of Mathematics, School of Science, Wuhan University of Technology, Wuhan, ChinaDepartment of Mathematics, School of Science, Wuhan University of Technology, Wuhan, ChinaThis paper concerns the multi-UAV cooperative path planning problem, which is solved by multi-objective optimization and by an adaptive evolutionary multi-objective estimation of distribution algorithm (AEMO-EDA). Since the traditional multi-objective optimization algorithms tend to fall into local optimum solutions when dealing with optimization problems in three dimensions, we suggest an advanced estimation of distribution algorithm. The main idea of this algorithm is to integrate the adaptive deflation of the selection rate, adaptive evolution of the covariance matrix, comprehensive evaluation of individual convergence and diversity, and reference point-based non-dominated ranking. A multi-UAV path planning model involving multi-objective optimization is established, and the designed algorithm is simulated and compared with other three high-dimensional multi-objective optimization algorithms. The results show that the AEMO-EDA proposed in this paper has stronger convergence and wider population distribution diversity in applying to the multi-UAV cooperative path planning model, as well as better global convergence. The algorithm can provide an stable path for each UAV and promote the intelligent operation of the UAV system.https://ieeexplore.ieee.org/document/10108001/Multiple UAVscollaborative path planningmulti-objective optimizationestimation of distribution algorithmsevolutionary algorithm |
| spellingShingle | Ren Yuhang Zhang Liang An Adaptive Evolutionary Multi-Objective Estimation of Distribution Algorithm and Its Application to Multi-UAV Path Planning IEEE Access Multiple UAVs collaborative path planning multi-objective optimization estimation of distribution algorithms evolutionary algorithm |
| title | An Adaptive Evolutionary Multi-Objective Estimation of Distribution Algorithm and Its Application to Multi-UAV Path Planning |
| title_full | An Adaptive Evolutionary Multi-Objective Estimation of Distribution Algorithm and Its Application to Multi-UAV Path Planning |
| title_fullStr | An Adaptive Evolutionary Multi-Objective Estimation of Distribution Algorithm and Its Application to Multi-UAV Path Planning |
| title_full_unstemmed | An Adaptive Evolutionary Multi-Objective Estimation of Distribution Algorithm and Its Application to Multi-UAV Path Planning |
| title_short | An Adaptive Evolutionary Multi-Objective Estimation of Distribution Algorithm and Its Application to Multi-UAV Path Planning |
| title_sort | adaptive evolutionary multi objective estimation of distribution algorithm and its application to multi uav path planning |
| topic | Multiple UAVs collaborative path planning multi-objective optimization estimation of distribution algorithms evolutionary algorithm |
| url | https://ieeexplore.ieee.org/document/10108001/ |
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