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

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorSong, Yanjie
dc.contributor.authorSuganthan, Ponnuthurai Nagaratnam
dc.contributor.authorPedrycz, Witold
dc.contributor.authorYan, Ran
dc.contributor.authorFan, Dongming
dc.contributor.authorZhang, Yue
dc.date.accessioned2025-04-18T09:38:12Z
dc.date.available2025-04-18T09:38:12Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractThe 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.
dc.identifier.citationSong, 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.
dc.identifier.doi10.1109/TAES.2024.3371964
dc.identifier.endpage4087
dc.identifier.issn00189251
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85187973736
dc.identifier.scopusqualityQ1
dc.identifier.startpage4073
dc.identifier.urihttp://dx.doi.org/10.1109/TAES.2024.3371964
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6826
dc.identifier.volume60
dc.identifier.wosWOS:001291141500056
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Transactions on Aerospace and Electronic Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectEnergy-Efficient
dc.subjectEnsemble of Local Search (LS) Methods
dc.subjectHeuristic
dc.subjectMemetic Algorithm (MA)
dc.subjectReinforcement Learning (RL)
dc.subjectSatellite Range Scheduling
dc.titleEnergy-Efficient Satellite Range Scheduling Using a Reinforcement Learning-Based Memetic Algorithm
dc.typeArticle

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