Generalized Model and Deep Reinforcement Learning-Based Evolutionary Method for Multitype Satellite Observation Scheduling

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dc.authoridWang, Xinwei/0000-0003-4988-222X
dc.authoridXing, Lining/0000-0002-6983-4244
dc.authoridYue, Zhang/0000-0002-5656-7642
dc.authoridSuganthan, Ponnuthurai Nagaratnam/0000-0003-0901-5105
dc.authoridOu, Junwei/0000-0001-8769-0953
dc.authorwosidWang, Xinwei/AAT-8080-2021
dc.authorwosidSuganthan, Ponnuthurai Nagaratnam/A-5023-2011
dc.contributor.authorSong, Yanjie
dc.contributor.authorOu, Junwei
dc.contributor.authorPedrycz, Witold
dc.contributor.authorSuganthan, Ponnuthurai Nagaratnam
dc.contributor.authorWang, Xinwei
dc.contributor.authorXing, Lining
dc.contributor.authorZhang, Yue
dc.date.accessioned2024-05-19T14:40:15Z
dc.date.available2024-05-19T14:40:15Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractMultitype satellite observation, including optical observation satellites, synthetic aperture radar (SAR) satellites, and electromagnetic satellites, has become an important direction in integrated satellite applications due to its ability to cope with various complex situations. In the multitype satellite observation scheduling problem (MTSOSP), the constraints involved in different types of satellites make the problem challenging. This article proposes a mixed-integer programming model and a generalized profit representation method in the model to effectively cope with the situation of multiple types of satellite observations. To obtain a suitable observation plan, a deep reinforcement learning-based genetic algorithm (DRL-GA) is proposed by combining the learning method and genetic algorithm. The DRL-GA adopts a solution generation method to obtain the initial population and assist with local search. In this method, a set of statistical indicators that consider resource utilization and task arrangement performance are regarded as states. By using deep neural networks to estimate the $Q$ value of each action, this method can determine the preferred order of task scheduling. An individual update strategy and an elite strategy are used to enhance the search performance of DRL-GA. Simulation results verify that DRL-GA can effectively solve the MTSOSP and outperforms the state-of-the-art algorithms in several aspects. This work reveals the advantages of the proposed generalized model and scheduling method, which exhibit good scalability for various types of observation satellite scheduling problems.en_US
dc.description.sponsorshipScience and Technology Innovation Team of Shaanxi Provinceen_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.1109/TSMC.2023.3345928
dc.identifier.issn2168-2216
dc.identifier.issn2168-2232
dc.identifier.scopus2-s2.0-85182921903en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1109/TSMC.2023.3345928
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4932
dc.identifier.wosWOS:001167324900001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions on Systems Man Cybernetics-Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectCombinatorial Optimization Problemen_US
dc.subjectDeep Reinforcement Learning (Drl)en_US
dc.subjectEvolutionary Algorithm (Ea)en_US
dc.subjectGeneralized Modelen_US
dc.subjectMultitypeen_US
dc.subjectSatellite Observationen_US
dc.subjectSchedulingen_US
dc.titleGeneralized Model and Deep Reinforcement Learning-Based Evolutionary Method for Multitype Satellite Observation Schedulingen_US
dc.typeArticleen_US

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