Energy-Efficient Satellite Range Scheduling Using a Reinforcement Learning-Based Memetic Algorithm
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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 (LS) 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 LS stage, multiple random and heuristic LS operators are incorporated through an ensemble LS strategy. 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 LS 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 LS operators can significantly enhance the algorithm's exploit capability. Moreover, this LS approach applies to solving other types of satellite scheduling problems. © 1965-2011 IEEE.
Açıklama
Anahtar Kelimeler
Energy-Efficient, Ensemble of Local Search (LS) Methods, Heuristic, Memetic Algorithm (MA), Reinforcement Learning (RL), Satellite Range Scheduling
Kaynak
IEEE Transactions on Aerospace and Electronic Systems
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
60
Sayı
4
Künye
Song, Y., Suganthan, P. N., Pedrycz, W., Yan, R., Fan, D., & Zhang, Y. (2024). Energy-Efficient Satellite Range Scheduling Using A Reinforcement Learning-based Memetic Algorithm. IEEE Transactions on Aerospace and Electronic Systems.
