Numerical Simulation Algorithm Study on Dynamic Programming of Training Airspace
The dynamic planning of the training airspace is of great significance for improving the utilization rate of the airspace, improving the efficiency of military training, and alleviating the contradiction between military and civilian air. The spatial dynamic programming problem is processed in stage...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Advances in Aeronautical Science and Engineering
2020-04-01
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| Series: | Hangkong gongcheng jinzhan |
| Subjects: | |
| Online Access: | http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2019063?st=article_issue |
| _version_ | 1811204469689417728 |
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| author | ZHANG Jianxiang GAN Xusheng SUN Jingjuan YANG Guozhou |
| author_facet | ZHANG Jianxiang GAN Xusheng SUN Jingjuan YANG Guozhou |
| author_sort | ZHANG Jianxiang |
| collection | DOAJ |
| description | The dynamic planning of the training airspace is of great significance for improving the utilization rate of the airspace, improving the efficiency of military training, and alleviating the contradiction between military and civilian air. The spatial dynamic programming problem is processed in stages, and the total occupation time is minimized by the optimal scheme of each stage. Aiming at the dynamic programming problem in each stage, on the basis of analyzing the complexity of the problem, the spatial planning model is constructed, and the genetic-discrete particle swarm optimization(DPSO) algorithm is proposed. By integrating the crossover and mutation ideas in the genetic algorithm, the DPSO algorithm’s ability to get rid of the local optimal solution is improved, and the convergence speed and accuracy of the algorithm are improved. At the same time, in order to ensure the diversity of population, the adaptive crossover operator and mutation operator are designed to ensure the individual feasibility, and the Gantt chart is used to represent the whole spatial planning process. Finally, the improved genetic-particle swarm optimization algorithm is used as an example. Compared with the traditional particle swarm optimization, the results show that the algorithm is of better results and faster convergence speed. |
| first_indexed | 2024-04-12T03:14:53Z |
| format | Article |
| id | doaj.art-fae341629d1b435194530e41f9912170 |
| institution | Directory Open Access Journal |
| issn | 1674-8190 |
| language | zho |
| last_indexed | 2024-04-12T03:14:53Z |
| publishDate | 2020-04-01 |
| publisher | Editorial Department of Advances in Aeronautical Science and Engineering |
| record_format | Article |
| series | Hangkong gongcheng jinzhan |
| spelling | doaj.art-fae341629d1b435194530e41f99121702022-12-22T03:50:14ZzhoEditorial Department of Advances in Aeronautical Science and EngineeringHangkong gongcheng jinzhan1674-81902020-04-0111219920610.16615/j.cnki.1674-8190.2020.02.00720200207Numerical Simulation Algorithm Study on Dynamic Programming of Training AirspaceZHANG Jianxiang0GAN Xusheng1SUN Jingjuan2YANG Guozhou3College of Science, Xijing University, Xi'an 710123, ChinaCollege of Air Traffic Control and Navigation, Air Force Engineering University, Xi'an 710051, ChinaCollege of Air Traffic Control and Navigation, Air Force Engineering University, Xi'an 710051, ChinaCollege of Air Traffic Control and Navigation, Air Force Engineering University, Xi'an 710051, ChinaThe dynamic planning of the training airspace is of great significance for improving the utilization rate of the airspace, improving the efficiency of military training, and alleviating the contradiction between military and civilian air. The spatial dynamic programming problem is processed in stages, and the total occupation time is minimized by the optimal scheme of each stage. Aiming at the dynamic programming problem in each stage, on the basis of analyzing the complexity of the problem, the spatial planning model is constructed, and the genetic-discrete particle swarm optimization(DPSO) algorithm is proposed. By integrating the crossover and mutation ideas in the genetic algorithm, the DPSO algorithm’s ability to get rid of the local optimal solution is improved, and the convergence speed and accuracy of the algorithm are improved. At the same time, in order to ensure the diversity of population, the adaptive crossover operator and mutation operator are designed to ensure the individual feasibility, and the Gantt chart is used to represent the whole spatial planning process. Finally, the improved genetic-particle swarm optimization algorithm is used as an example. Compared with the traditional particle swarm optimization, the results show that the algorithm is of better results and faster convergence speed.http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2019063?st=article_issuedynamic programmingtraining airspacegenetic algorithmparticle swarm optimization algorithm |
| spellingShingle | ZHANG Jianxiang GAN Xusheng SUN Jingjuan YANG Guozhou Numerical Simulation Algorithm Study on Dynamic Programming of Training Airspace Hangkong gongcheng jinzhan dynamic programming training airspace genetic algorithm particle swarm optimization algorithm |
| title | Numerical Simulation Algorithm Study on Dynamic Programming of Training Airspace |
| title_full | Numerical Simulation Algorithm Study on Dynamic Programming of Training Airspace |
| title_fullStr | Numerical Simulation Algorithm Study on Dynamic Programming of Training Airspace |
| title_full_unstemmed | Numerical Simulation Algorithm Study on Dynamic Programming of Training Airspace |
| title_short | Numerical Simulation Algorithm Study on Dynamic Programming of Training Airspace |
| title_sort | numerical simulation algorithm study on dynamic programming of training airspace |
| topic | dynamic programming training airspace genetic algorithm particle swarm optimization algorithm |
| url | http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2019063?st=article_issue |
| work_keys_str_mv | AT zhangjianxiang numericalsimulationalgorithmstudyondynamicprogrammingoftrainingairspace AT ganxusheng numericalsimulationalgorithmstudyondynamicprogrammingoftrainingairspace AT sunjingjuan numericalsimulationalgorithmstudyondynamicprogrammingoftrainingairspace AT yangguozhou numericalsimulationalgorithmstudyondynamicprogrammingoftrainingairspace |