Energy-Efficient Satellite Range Scheduling Using a Reinforcement Learning-Based Memetic Algorithm

The rapid expansion of the satellite industry has presented numerous opportunities across various sectors and significantly transformed people's daily lives. However, the high energy consumption resulting from frequent task execution poses challenges for satellite management. Energy consumption has become an important factor to be considered in the design of future satellite management systems. The energy-efficient satellite range scheduling problem (EESRSP) aims to optimize task sequencing profits within the satellite management system while simultaneously conserving energy. To address this problem, a mixed-integer scheduling model is constructed, taking into account the energy consumption of ground stations during telemetry, tracking and command (TT&C) operations. Then, we propose a reinforcement learning-based memetic algorithm (RL-MA) that incorporates a heuristic initialization method (HIM). The HIM enables the algorithm to rapidly generate high-quality initial solutions by leveraging task features associated with EESRSRP. RL-MA employs both population search and local search techniques to explore the satellite TT&C task plan. RL-MA incorporates two genetic operators, crossover and mutation, into the population-based search. In the local search stage, multiple random and heuristic local search operators are incorporated through an ensemble local search strategy (ELSS). To improve search performance, Q-learning, a classical class of reinforcement learning (RL) methods tailored to problem characteristics, is utilized for selecting effective operators. RL dynamically adjusts local search operators based on strategy performance. Experimental results demonstrate that the proposed RL-MA can effectively generate sound solutions for EESRSP with varying task scales. Furthermore, the improvement strategies employed in the algorithm are validated to enhance the scheduling performance of RL-MA. This study reveals that integrating RL with an ensemble of local search operators can significantly enhance the algorithm's exploit capability. Moreover, this local search approach applies to solving other types of satellite scheduling problems.