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Multi-Adaptive Strategies-Based Higher-Order Quantum Genetic Algorithm for Agile Remote Sensing Satellite Scheduling

Xiaohan Sun1,2, Yuan Ren3, Linghui Yu2

  • 1Department of Aerospace Engineering and Technology, Space Engineering University, Beijing 101416, China.

Sensors (Basel, Switzerland)
|August 10, 2024
PubMed
Summary

A new algorithm, MAS-HOQGA, efficiently solves large-scale agile remote sensing satellite scheduling problems. It overcomes limitations of traditional methods by improving global optimization and reducing computation time for complex satellite tasks.

Keywords:
adaptive strategiesagile remote sensing satellite schedulingglobal optimizationlarge-scale tasksquantum genetic algorithm

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Area of Science:

  • Satellite Technology
  • Operations Research
  • Artificial Intelligence

Background:

  • The agile remote sensing satellite scheduling problem (ARSSSP) involves complex constraints and vast solution spaces.
  • Existing methods often struggle with efficiency and avoiding local optima in large-scale scheduling tasks.

Purpose of the Study:

  • To propose a novel algorithm, the Multi-Adaptive Strategies-based Higher-Order Quantum Genetic Algorithm (MAS-HOQGA), for ARSSSP.
  • To enhance scheduling efficiency and global optimization capabilities for large-scale remote sensing satellite tasks.

Main Methods:

  • Developed a satellite scheduling model integrating time-dependent characteristics, maneuverability, energy, and data storage constraints.
  • Introduced quantum register operators, adaptive evolution, and adaptive mutation transfer operations into a quantum genetic algorithm framework.
  • Combined total task number and priority as the optimization objective for scheduling schemes.

Main Results:

  • The MAS-HOQGA demonstrated high computational efficiency and excellent global optimization ability.
  • The proposed method effectively avoids the local optima and low solution efficiency issues of traditional Quantum Genetic Algorithms (QGA).
  • Experimental results validate the algorithm's performance on large-scale ARSSSP.

Conclusions:

  • MAS-HOQGA offers a superior approach for solving complex, large-scale agile remote sensing satellite scheduling problems.
  • The algorithm's efficiency and global optimization capabilities make it suitable for practical engineering applications.
  • This research contributes a robust solution for optimizing remote sensing satellite task scheduling.