Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm
On the basis that satellites given fixed count and orbit elements can be served in bounded time when an on-orbit serving mission order is set at any uncertain time in a given time interval, the deployment of on-orbit service vehicle (OSV) serving satellites becomes a complex multiple nested optimiza...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/6644339 |
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| _version_ | 1849304840610512896 |
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| author | Yaxiong Li Xinglong Sun Xinxue Liu Jian Wu Qingguo Liu |
| author_facet | Yaxiong Li Xinglong Sun Xinxue Liu Jian Wu Qingguo Liu |
| author_sort | Yaxiong Li |
| collection | DOAJ |
| description | On the basis that satellites given fixed count and orbit elements can be served in bounded time when an on-orbit serving mission order is set at any uncertain time in a given time interval, the deployment of on-orbit service vehicle (OSV) serving satellites becomes a complex multiple nested optimization problem, and the essence of deployment is to determine the count and orbit elements of OSVs. In consideration of the characteristics of this deployment problem, we propose a fuzzy adaptive particle swarm optimization (FAPSO) algorithm to solve this problem. First, on the basis of double pulse rendezvous hypothesis, a transfer optimization model of a single OSV serving multiple satellites is established based on genetic algorithm (GA), and this is used to compute the indexes of the subsequent two optimization models. Second, an assignment optimization model of OSVs is established based on the discrete particle swarm optimization (DPSO) algorithm, laying the foundation of the next optimization model. Finally, the FAPSO algorithm, which improves the performance of PSO algorithm by adjusting the inertia weight, is proposed to solve the deployment problem of multiple OSVs. The simulation results demonstrate that all optimization models in this study are feasible, and the FAPSO algorithm, which has a better convergence result than that obtained using the other optimization algorithms, can effectively solve the deployment problem of OSVs. |
| format | Article |
| id | doaj-art-3a5c250c22b24093b04b9dfa6b3ce67b |
| institution | Kabale University |
| issn | 1687-5591 1687-5605 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj-art-3a5c250c22b24093b04b9dfa6b3ce67b2025-08-20T03:55:37ZengWileyModelling and Simulation in Engineering1687-55911687-56052021-01-01202110.1155/2021/66443396644339Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization AlgorithmYaxiong Li0Xinglong Sun1Xinxue Liu2Jian Wu3Qingguo Liu4Xi’an High-Tech Institute, Xi’an 710025, ChinaXi’an High-Tech Institute, Xi’an 710025, ChinaXi’an High-Tech Institute, Xi’an 710025, ChinaXi’an High-Tech Institute, Xi’an 710025, ChinaXi’an High-Tech Institute, Xi’an 710025, ChinaOn the basis that satellites given fixed count and orbit elements can be served in bounded time when an on-orbit serving mission order is set at any uncertain time in a given time interval, the deployment of on-orbit service vehicle (OSV) serving satellites becomes a complex multiple nested optimization problem, and the essence of deployment is to determine the count and orbit elements of OSVs. In consideration of the characteristics of this deployment problem, we propose a fuzzy adaptive particle swarm optimization (FAPSO) algorithm to solve this problem. First, on the basis of double pulse rendezvous hypothesis, a transfer optimization model of a single OSV serving multiple satellites is established based on genetic algorithm (GA), and this is used to compute the indexes of the subsequent two optimization models. Second, an assignment optimization model of OSVs is established based on the discrete particle swarm optimization (DPSO) algorithm, laying the foundation of the next optimization model. Finally, the FAPSO algorithm, which improves the performance of PSO algorithm by adjusting the inertia weight, is proposed to solve the deployment problem of multiple OSVs. The simulation results demonstrate that all optimization models in this study are feasible, and the FAPSO algorithm, which has a better convergence result than that obtained using the other optimization algorithms, can effectively solve the deployment problem of OSVs.http://dx.doi.org/10.1155/2021/6644339 |
| spellingShingle | Yaxiong Li Xinglong Sun Xinxue Liu Jian Wu Qingguo Liu Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm Modelling and Simulation in Engineering |
| title | Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm |
| title_full | Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm |
| title_fullStr | Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm |
| title_full_unstemmed | Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm |
| title_short | Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm |
| title_sort | deployment of on orbit service vehicles using a fuzzy adaptive particle swarm optimization algorithm |
| url | http://dx.doi.org/10.1155/2021/6644339 |
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