基于引力遗传搜索算法的低轨遥感卫星任务快速规划方法

岳群彬; 陈亚军; 林晓勇; 张艳红; 付伟; 高朝晖

doi:10.11959/j.issn.1000-0801.2025037

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基于引力遗传搜索算法的低轨遥感卫星任务快速规划方法

研究 | 更新时间：2026-01-09

- 基于引力遗传搜索算法的低轨遥感卫星任务快速规划方法
- Fast Planning Method for LEO Remote Sensing Satellite Missions Using the Gravitational Genetic Search Algorithm
- 天地一体化信息网络 2025年6卷第4期页码：36-50
- 作者机构：
  
  1.中国电子科技集团公司第五十四研究所，河北石家庄 050081
  2.智能空间信息国家级重点实验室，北京 100029
- 作者简介：
  
  [ "岳群彬（1980- ），男，中国电子科技集团公司第五十四研究所正高级工程师，主要研究方向为航天测控与运控、卫星任务规划。" ]
  陈亚军（1985-），男，中国电子科技集团公司第五十四研究所高级工程师，主要研究方向为航天测控与运控、卫星任务规划。
  林晓勇（1985-），男，中国电子科技集团公司第五十四研究所高级工程师，主要研究方向为航天测控与运控、卫星任务规划。
  张艳红（1991-），女，智能空间信息国家级重点实验室助理工程师，主要研究方向为大数据、信息处理应用。
  付伟（1986-），男，中国电子科技集团公司第五十四研究所高级工程师，主要研究方向为航天测控与运控、卫星任务规划。
  高朝晖（1972-），男，中国电子科技集团公司第五十四研究所正高级工程师，主要研究方向为航天测控与运控、卫星任务规划。
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2025037
  中图分类号： TP79
- 收稿：2025-04-21，
  
  修回：2025-07-28，
  
  纸质出版：2025-12-20
- 稿件说明：
移动端阅览
岳群彬,陈亚军,林晓勇等.基于引力遗传搜索算法的低轨遥感卫星任务快速规划方法[J].天地一体化信息网络,2025,06(04):36-50.

YUE Qunbin,CHEN Yajun,LIN Xiaoyong,et al.Fast Planning Method for LEO Remote Sensing Satellite Missions Using the Gravitational Genetic Search Algorithm[J].Space-Integrated-Ground Information Networks,2025,06(04):36-50.
岳群彬,陈亚军,林晓勇等.基于引力遗传搜索算法的低轨遥感卫星任务快速规划方法[J].天地一体化信息网络,2025,06(04):36-50. DOI： 10.11959/j.issn.1000-0801.2025037.

YUE Qunbin,CHEN Yajun,LIN Xiaoyong,et al.Fast Planning Method for LEO Remote Sensing Satellite Missions Using the Gravitational Genetic Search Algorithm[J].Space-Integrated-Ground Information Networks,2025,06(04):36-50. DOI： 10.11959/j.issn.1000-0801.2025037.

摘要

针对遥感卫星任务规划这一NP-hard问题，提出一种新型引力遗传搜索算法（GGSA）。该算法融合引力搜索算法的快速收敛特性与遗传算法的全局搜索能力，并通过3阶段混合策略（引力搜索、精英交叉、定向变异）优化求解过程。同时，在理论层面给出了收敛性证明和复杂度分析

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))，在应用层面明确了适用于低轨光学遥感卫星的布设条件。6类场景下的仿真验证表明，本文所提算法能够高效处理复杂的遥感卫星任务规划问题，在收敛速度和搜索精度方面具有显著优势。

Abstract

To address the NP-hard problem of remote sensing satellite mission planning

this paper proposes a novel gravitational genetic search algorithm (GGSA). This algorithm integrates the rapid convergence capability of the gravitational search algorithm (GSA) with the global exploration strength of the genetic algorithm (GA)

and optimizes the solution process through a three-phase hybrid strategy (gravitational search

elite crossover

and directional mutation). At the theoretical level

convergence proof and complexity analysis O(

×(

)) are provided. At the application level

the algorithm's suitability for deployment conditions involving low earth orbit (LEO) optical re

mote sensing satellites is clarified. Simulation results across six distinct scenario types demonstrate that GGSA exhibits superior capability in handling complex remote sensing satellite mission planning problems

achieving significant advantages in both convergence speed and search accuracy.

关键词

Keywords

references

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