| 基于任务合成机制的多星调度问题 |
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| 引用本文： | 任送莲,孙海权,靳鹏. 基于任务合成机制的多星调度问题[J]. 系统工程与电子技术, 2021, 43(1): 171-180. DOI: 10.3969/j.issn.1001-506X.2021.01.21 |
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| 作者姓名： | 任送莲 孙海权 靳鹏 |
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| 作者单位： | 1. 合肥工业大学管理学院, 安徽 合肥 2300092. 过程优化与智能决策教育部重点实验室, 安徽 合肥 230009 |
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| 基金项目： | 国家自然科学基金(71671059);国家自然科学基金(71521001);国家自然科学基金(71472058) |
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| 摘 要： | 传统模式下,卫星采取单任务观测方式,该种方式下任务的成像精度高但任务成像数量少且资源使用率极低。因此,在单任务观测方式的基础上设计了一种多任务合成机制(multi-task merging mechanism, MTMM),在保证用户最低成像要求的情况下对任务合成。首先,基于合成任务集,建立多星调度模型。然后，针对模型提出了基于任务合成的改进蚁群优化(improved ant colony optimization based on task merging, IACO-TM)算法,在算法中设计了自适应蚁窗策略、强制扰动机制以及算法参数动态调节策略,对蚂蚁搜索空间进行有效裁剪,避免算法陷入局部最优的同时提高算法的收敛速度。最后,通过大量仿真实验与不考虑任务合成的改进蚁群优化(improved ant colony optimization, IACO)算法和基于任务合成的传统蚁群优化(traditional ant colony optimization based on task merging, TACO-TM)算法对比,验证了所提MTMM和IACO-TM的有效性。
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| 关 键 词： | 多星调度 任务合成 蚁群算法 自适应 |
| 收稿时间： | 2020-04-10 |
Multi-satellite scheduling problem based on task merging mechanism |
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| Songlian REN,Haiquan SUN,Peng JIN. Multi-satellite scheduling problem based on task merging mechanism[J]. System Engineering and Electronics, 2021, 43(1): 171-180. DOI: 10.3969/j.issn.1001-506X.2021.01.21 |
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| Authors: | Songlian REN Haiquan SUN Peng JIN |
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| Affiliation: | 1. School of Management, Hefei University of Technology, Hefei 230009, China2. Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, China |
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| Abstract: | In the traditional pattern, the satellite adopts the single task observation mode, in which the imaging accuracy of the task is high. However, the imaging quantity of the task is small and the utilization rate of the resource is extremely low. Therefore, a multi-task merging mechanism (MTMM) based on the single task observation mode is designed, which adopts the way of task merging in the case of ensuring the minimum imaging requirements of users. Firstly, on the basis of the merging task set, a multi-satellite scheduling model is established, and then an algorithm of improved ant colony optimization based on task merging (IACO-TM) is proposed for the model. In the algorithm, an adaptive ant window strategy, a forced disturbance mechanism and a parameters dynamic adjustment strategy of the algorithm are designed, so as to cut the ant search space effectively, avoid the algorithm falling into the local optimum and improve the convergence speed of the algorithm at the same time. Finally, a large number of simulation experiments are provided to verify the effectiveness of MTMM and IACO-TM, comparing with the algorithm of improved ant colony optimization (IACO) and the algorithm of traditional ant colony optimization based on task merging (TACO-TM). |
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| Keywords: | multi-satellite scheduling task merging ant colony algorithm self-adaption |
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