A Reinforcement Learning-Enhanced Dung Beetle Optimization Approach for Agile Earth Observation Satellite Scheduling

Weiquan Huang, He Wang, Junyu Wu, Haoyu Hou, Jiatao Li, Zixuan Li, Yanjie Song

Abstract: Due to the increasing demand for remote sensing imaging products, the agile Earth observation satellite scheduling problem (AEOSSP) has garnered significant attention. In response, this letter proposes a reinforcement learning-based dung beetle optimization (RLDBO) algorithm to address the AEOSSP. The proposed method dynamically adjusts the proportions of four types of dung beetles (ball-rolling beetle, brood ball beetle, small dung beetle, and thief beetle), enabling adaptive optimization of the scheduling scheme to better handle the complexities and uncertainties of the search space. The Q-learning mechanism guides the adjustment of these proportions, effectively balancing global exploration and local exploitation at different stages of the search process. Experimental results demonstrate that the RLDBO algorithm effectively solves the AEOSSP across multiple instances, and it outperforms other algorithms in various aspects, including optimization performance, convergence speed, and scheduling effectiveness. The experimental validation confirms that RLDBO significantly enhances the efficiency and effectiveness of agile Earth observation satellite (AEOS) scheduling.

External IDs:doi:10.1109/lgrs.2025.3527925