面向星地协同观测规划问题的改进人工蜂群算法
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作者单位:

1.国防科技大学系统工程学院;2.32036部队

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中图分类号:

TP391

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Improved Artificial Bee Colony Algorithm for Satellite-Ground Cooperative Observation Planning Problem
Author:
Affiliation:

College of Systems Engineering, National University of Defense Technology

Fund Project:

National Natural Science Foundation of China

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    摘要:

    卫星和地面观测资源利用各自搭载载荷可以完成灾害预报、环境监测、目标发现等任务。地面观测资源可以与卫星配合共同完成观测任务,提升任务观测效果。通过规划将众多的观测任务分配给有限的卫星、地面观测资源来执行就可以让协同观测发挥作用。想要同时得出卫星和地面观测资源的协同任务执行方案对任务规划提出了很高的要求。本文对星地协同观测规划问题(SGCOPP)进行研究,构建了协同观测规划的数学模型,并根据问题特点提出了一种改进的人工蜂群算法(IABC)和一种卫星-地面资源协同时间选择算法(SGRCTSA)。改进的人工蜂群算法在初始种群生成、蜂群优化过程、个体淘汰过程等过程进行改进以提升算法的搜索优化能力。之后,通过大量的实验验证了提出的改进人工蜂群算法求解星地协同观测规划问题的有效性, 求解结果好于用于对比的基准算法。本文研究成果可以为跨域协同观测研究提供技术支持。

    Abstract:

    Satellites and ground observation resources can use their respective loads to complete tasks such as disaster forecasting, environmental monitoring, and target discovery. Ground observation resources can cooperate with satellites to complete observation tasks and improve task observation effects. The planning algorithm allocates numerous observation tasks to limited satellites and ground observation resources for execution, which can improve the observation effect. It is challenge to obtain a mission execution plan for satellite and ground observation resources at the same time. This paper studies the satellite-ground coordinated observation planning problem (SGCOPP), constructs a mathematical model of mission planning, and proposes an improved artificial bee colony algorithm (IABC) and a satellite-ground resource coordinated time selection algorithm based on the characteristics of the problem (SGRCTSA). This algorithm improves in the process of initial population generation, colony optimization process, and individual elimination process to enhance the search optimization ability of the algorithm. After that, we verified the effectiveness of the proposed IABC algorithm to solve SGCOPP problem through a large number of experiments. Results show that IABC algorithm performs better than state-of-the-art algorithms. Research results can provide technical support for cross-domain collaborative observation research.

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历史
  • 收稿日期:2020-10-23
  • 最后修改日期:2020-12-05
  • 录用日期:2020-12-25
  • 在线发布日期: 2021-02-04
  • 出版日期: